Risk assessment method, system and device for power distribution topology dynamic reconfiguration and medium

Abstract

A power distribution topology dynamic reconstruction risk assessment method, system, device and medium, relates to the technical field of data processing. The method comprises: acquiring intelligent component real-time state data to construct an initial topology structure, and identifying the target node that has changed by sending a detection signal. Then dynamically reconstruct the topology structure in three logical layers. The electrical characteristics of the reconstructed topology structure are checked. After the electrical characteristics check, the connection relationship between the power distribution cabinet and the component link of the target node is determined, and the risk level is calculated based on the historical operation parameters. According to the risk level, temporary countermeasures are formulated, and a risk assessment report containing the topology influence range, risk level and countermeasures is generated. The whole process realizes real-time monitoring and risk warning of the topology change of the power distribution system. The implementation of the technical solution provided in the application can improve the risk assessment accuracy of power distribution topology dynamic reconstruction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a risk assessment method, system, device and medium for power distribution topology dynamic reconstruction. BACKGROUND

[0002] With the continuous advancement of smart grid construction, power distribution systems are gradually developing towards intelligentization and automation, and various intelligent components are widely used in power distribution systems. The system topology structure also dynamically changes with the needs of power load distribution, equipment maintenance, etc.

[0003] At present, the power distribution system adopts a risk assessment method based on state monitoring, which analyzes the system operation state by collecting the operation data of intelligent components, and performs risk assessment when detecting topology structure changes.

[0004] However, in actual application, due to the increasing complexity of the power distribution system structure, the existing evaluation method often treats the changes in the power distribution system as independent events, and fails to fully consider the correlation between different components in the power distribution system. The evaluation result lacks hierarchical analysis, resulting in insufficient accuracy of risk assessment for power distribution topology dynamic reconstruction, which affects the safe and stable operation of the power distribution system. SUMMARY

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

[0006] In a first aspect, the present application provides a risk assessment method for power distribution topology dynamic reconstruction, comprising:

[0007] Obtaining real-time state data of intelligent components in a power distribution system, and constructing a first topology structure of the power distribution system based on the real-time state data;

[0008] Sending a detection signal to each topology node in the first topology structure, and determining any topology node as a target node of topology change when no feedback signal of the any topology node is received for a continuous preset number of times;

[0009] According to the topology change type of the target node, dynamically reconstructing the first topology structure at multiple logical layers to obtain a second topology structure, the logical layers including a microscopic layer, a mesoscopic layer and a macroscopic layer;

[0010] Performing electrical characteristic verification on any closed loop in the second topology structure;

[0011] when the electrical characteristic verification passes, determining, based on the association relationship in the second topological structure, a power distribution cabinet connection relationship in an upstream direction and a component association link in a downstream direction from the target node, determining a topological influence range of the power distribution cabinet connection relationship and the component association link, and calculating a risk level in combination with historical operation parameters of intelligent components in the topological influence range;

[0012] generating a corresponding temporary coping strategy according to the risk level;

[0013] generating a risk assessment report of the power distribution system based on the topological influence range, the risk level, and the temporary coping strategy.

[0014] By adopting the above technical solution, the real-time state data of intelligent components in the power distribution system is acquired to construct a first topological structure, a target node where a topological change occurs is determined through a detection signal, and then the first topological structure is dynamically reconstructed to obtain a second topological structure at multiple logical layers of a micro layer, a meso layer, and a macro layer. The electrical characteristic verification is performed on the second topological structure, the topological influence range of the power distribution cabinet connection relationship and the component association link is determined based on the association relationship in the second topological structure after the verification passes, and the risk level is calculated in combination with the historical operation parameters of intelligent components in the range. Then, the temporary coping strategy and the risk assessment report are generated, so as to realize the multi-level analysis of the topological structure change of the power distribution system, fully consider the association relationship between different components, improve the risk assessment accuracy of the power distribution topological dynamic reconstruction, and be beneficial to the safe and stable operation of the power distribution system.

[0015] Optionally, static data and dynamic data stored in a built-in topological protocol chip of each intelligent component are acquired, the static data includes a component unique identifier, a rated current, and an interface type, and the dynamic data includes an online state, a real-time working current, and a temperature; the target intelligent component in a powered-on state is identified according to the online state, and adjacent intelligent components of the target intelligent component are acquired based on the component unique identifier; for the adjacent intelligent components, when the matching degree of the rated current and the interface type is greater than a preset threshold, the adjacent intelligent components are determined as target adjacent intelligent components; the series and parallel connection relationship between the target adjacent intelligent components is established by taking the weighted value of the real-time working current and the temperature as an edge weight, and the first topological structure is generated.

[0016] Optionally, a logical layer system is constructed with a micro layer of intelligent components as nodes, a meso layer of internal loops in a power distribution cabinet as units, and a macro layer of a power distribution system as a whole; in the logical layer system, when the intelligent component corresponding to the target node changes, the connection relationship of the intelligent component with the upstream input node and the downstream output node is adjusted in the micro layer, and the meso layer and the macro layer remain unchanged; when any loop in the power distribution cabinet corresponding to the target node fails, the series or parallel logic of the intelligent component in the fault loop is deleted in the meso layer, and the external connection state of the power distribution cabinet in the macro layer is updated synchronously; when the feeder line between the power distribution cabinets corresponding to the target node fails, the feeder connection logic is reconstructed in the macro layer, and is synchronously transmitted downward to the meso layer, and the loop state of the power distribution cabinet is marked as offline.

[0017] Optionally, any closed loop in the second topological structure and the nodes in the closed loop are extracted; for the closed loop, the current flowing through each line section is collected, and the voltage drop vector of each line section is calculated based on the current flowing through each line section, and it is verified whether the sum of each voltage drop vector is zero; for the nodes, the branch current vector flowing into and out of the node is collected, and it is verified whether the sum of each branch current vector is zero; if the sum of each voltage drop vector and the sum of each branch current vector are zero, it is determined that the electrical characteristic verification is passed.

[0018] Optionally, the inlet circuit breaker identifier of the power distribution cabinet where the target node is located is obtained, the power distribution cabinet connection relationship is obtained by tracing upwards based on the inlet circuit breaker identifier until the total power distribution cabinet; based on the output port identifier of the target node, the downstream intelligent component connected with the target node is determined, the load branch of the downstream intelligent component is recursively obtained, and the component associated link is generated; according to the power distribution cabinet connection relationship and the component associated link, the upstream influence power distribution cabinet and the downstream influence intelligent component of the target node topological change are marked; the coverage range of the upstream influence power distribution cabinet and the downstream influence intelligent component is determined as the topological influence range.

[0019] Optionally, the output port identifier of the target node is traversed to obtain the downstream intelligent component corresponding to the output port identifier; the load type and load capacity of the downstream intelligent component are extracted, and the corresponding component cascade table is established; based on the component cascade table, recursive traversal is performed on the downstream intelligent component until the terminal load device is reached; each downstream intelligent component traversed by the recursive traversal is sorted according to the power supply sequence to obtain a plurality of load branches, and each load branch is mapped to a preset tree structure to obtain a component associated link.

[0020] Optionally, historical operation parameters of each intelligent component in the topology influence range within a preset time period are collected, the historical operation parameters including overload times, load types, rated power and real-time current; importance weights of each intelligent component in the topology influence range are determined according to the load types; a current load rate is calculated by calculating a ratio of the real-time current to the rated current of each intelligent component; a risk level score of a corresponding intelligent component is obtained by weighted calculation based on the importance weights, the current load rate and the overload times of each intelligent component; and for each intelligent component, a corresponding risk level is divided according to a numerical range of the risk level score.

[0021] In a second aspect of the present application, a risk assessment system for power distribution topology dynamic reconstruction is provided, and the system comprises:

[0022] A data acquisition module is configured to acquire real-time state data of intelligent components in a power distribution system, and construct a first topology structure of the power distribution system based on the real-time state data;

[0023] A dynamic reconstruction module is configured to send a detection signal to each topology node in the first topology structure, determine a target node that has a topology change when no feedback signal of any topology node is received for a continuous preset number of times, and dynamically reconstruct the first topology structure at multiple logical layers according to a topology change type of the target node to obtain a second topology structure, wherein the logical layers include a micro layer, a meso layer and a macro layer;

[0024] A risk level determination module is configured to calculate a port current sum of intelligent components in a closed loop in the second topology structure and a voltage drop sum of lines in the closed loop, verify whether a vector sum of the port current sum and each voltage drop sum satisfies a condition that the vector sum is zero, determine power distribution cabinet connection relationships in an upstream direction and component associated links in a downstream direction based on an association relationship in the second topology structure when the condition is satisfied, determine a topology influence range of the power distribution cabinet connection relationships and the component associated links, and calculate a risk level in combination with historical operation parameters of intelligent components in the topology influence range;

[0025] A risk assessment module is configured to generate a corresponding temporary coping strategy according to the risk level, and generate a risk assessment report of the power distribution system based on the topology influence range, the risk level and the temporary coping strategy.

[0026] In a third aspect of the present application, an electronic device is provided, which comprises a memory, a processor and a program stored in the memory and executable on the processor, and the program can be loaded and executed by the processor to implement a risk assessment method for power distribution topology dynamic reconstruction.

[0027] In a fourth aspect of the present application, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor implements a risk assessment method for power distribution topology dynamic reconstruction.

[0028] In summary, the one or more technical solutions provided by the present application have at least the following technical effects or advantages:

[0029] By adopting the above technical solution, the real-time state data of the intelligent components in the power distribution system is acquired to construct a first topology structure, and a target node that causes a topology change is determined through a detection signal. Then, the first topology structure is dynamically reconstructed at multiple logical layers of a micro layer, a meso layer and a macro layer to obtain a second topology structure. The second topology structure is subjected to electrical characteristic verification. After the verification is passed, the topology influence range of the power distribution cabinet connection relationship and the component correlation link is determined based on the correlation relationship in the second topology structure, and the risk level is calculated in combination with the historical operation parameters of the intelligent components in the range, and then a temporary response strategy and a risk assessment report are generated. Thus, multi-level analysis of the topology structure change of the power distribution system is realized, the correlation relationship between different components is fully considered, the risk assessment accuracy of the power distribution topology dynamic reconstruction is improved, and the safe and stable operation of the power distribution system is facilitated. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a flow diagram of a risk assessment method for power distribution topology dynamic reconstruction provided by an embodiment of the present application;

[0031] Figure 2 is a structural diagram of a risk assessment system for power distribution topology dynamic reconstruction provided by an embodiment of the present application;

[0032] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0033] Legend of reference signs: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0034] In order for those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the present specification will be described clearly and completely in conjunction with the drawings in the embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments.

[0035] In the description of the embodiments of the present application, the words "for example" or "for instance" are used to indicate an example, an instance, or an illustration. Any embodiment or design presented as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferable or advantageous than other embodiments or designs. In fact, the use of the words "for example" or "for instance" is intended to present the relevant concept in a specific manner.

[0036] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first", "second", "third", etc. are used only for the purpose of description and should not be construed as indicating or implying relative importance or implicitly indicating the technical features indicated. Therefore, the features defined with "first", "second", etc. can explicitly or implicitly include one or more of the features. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0037] The embodiments of the present application provide a risk assessment method for power distribution topology dynamic reconstruction. In one embodiment, referring to Figure 1 , Figure 1 is a flowchart of the risk assessment method for power distribution topology dynamic reconstruction provided by the embodiments of the present application. The method can be implemented by relying on a computer program, which can be integrated in an application or run as an independent tool class application. The method can also be implemented by relying on a single-chip microcomputer or run on a power distribution topology dynamic reconstruction risk assessment system based on the von Neumann system. Specifically, the method can include the following steps:

[0038] Step 101: Obtain real-time state data of intelligent components in the power distribution system, and construct a first topology structure of the power distribution system based on the real-time state data.

[0039] Among them, the intelligent component represents a power distribution device with data acquisition, communication and control functions, such as an intelligent circuit breaker, an intelligent switch, an intelligent meter, etc.; the real-time state data refers to the current working state information of the intelligent component, including switch state, current value, voltage value, temperature and other operating parameters; the first topology structure represents the network structure model of the physical connection relationship and the logical association relationship between the intelligent components in the power distribution system.

[0040] Specifically, during the operation of the power distribution system, the running state and the topology structure of the system need to be grasped in real time. This step continuously collects the real-time running data of each intelligent component through the data collection module of the power distribution automation system. After the collected data is preprocessed and screened, combined with the static attribute information (such as device type, rated parameter, etc.) of the intelligent component, a topology structure model reflecting the actual running state of the current power distribution system is constructed. This model not only contains the physical connection relationship between devices, but also includes the on-off state of each loop, the load distribution and other information, providing basic data support for subsequent topology reconstruction and risk assessment.

[0041] In some embodiments, the collection of real-time state data and the construction of topology structure can be realized in various ways: optionally, first, the power distribution automation master station system sends data collection instructions to each intelligent component, and the intelligent component uploads the current running state data to the master station system after receiving the instructions; then, the collected data is time-stamped and analyzed for data quality, and abnormal data is removed; finally, based on the device topology description file template, the qualified real-time data is filled into the corresponding data items to generate a complete topology structure model. Optionally, the running data is automatically collected by the built-in state monitoring module of the intelligent component, and the data is regularly reported to the data collection center through the communication network; the data collection center classifies and organizes the received data and processes them in a unified format; finally, using a graphical modeling tool, the processed data is converted into a standard topology structure description file. It can be understood that other data collection methods and topology construction methods can also be used to realize the function of this step, which is not limited here.

[0042] Based on the above embodiments, as an optional embodiment, in step 101, the first topology structure of the power distribution system is constructed based on the real-time state data. This step can also include the following steps:

[0043] Step 201: Obtain the static data and dynamic data stored in the built-in topology protocol chip of each intelligent component. The static data includes the unique identification of the component, the rated current and the interface type, and the dynamic data includes the online state, the real-time working current and the temperature; identify the target intelligent component in the power-on state according to the online state, and obtain the adjacent intelligent components of the target intelligent component based on the unique identification of the component.

[0044] Among them, the built-in topology protocol chip is a special chip integrated in the intelligent component for storing device information and performing communication functions; the static data refers to the basic attribute information inherent to the device, including the identification code for uniquely identifying the device, the rated working current value of the device and the physical and electrical characteristics of the device interface; the dynamic data refers to the real-time running state information of the device, including whether the device is in the power-on working state, the actual current value flowing through the device and the working temperature of the device.

[0045] Specifically, this step establishes a communication connection with the topology protocol chip built-in the intelligent component, and reads the device information stored in the chip. First, static data is read to obtain the unique identification code of the component (such as "SW001", "CB002", etc.), the rated current value (such as "400A"), and the interface type (such as "three-phase four-wire system"). Dynamic data is also read, including the online status flag of the device (1 represents online, 0 represents offline), the real-time current value, and the temperature value. The system filters out the intelligent components currently in the power-on state according to the online status flag, and marks these components as target intelligent components. For each target intelligent component, the system queries the device topology relationship database through its unique identification code to obtain the upstream and downstream device information physically connected to the component. For example, for a smart circuit breaker, the system will obtain the bus connection device upstream and the load device information downstream. These information provides basic data support for subsequent construction of complete power distribution system topology. In specific implementation, the system first establishes a data exchange channel with the topology protocol chip through standard communication protocols such as Modbus or IEC61850, and then reads various types of information in accordance with the predefined data structure. For the dynamic data obtained, the system will perform data validity verification to ensure the accuracy of the data. By analyzing the physical connection relationship between devices, the system establishes an adjacency relationship table of intelligent components, recording the direct connection information of each device.

[0046] Step 202: For adjacent intelligent components, when the matching degree of rated current and interface type is greater than a preset threshold, the adjacent intelligent components are determined as target adjacent intelligent components.

[0047] Wherein, the matching degree represents the adaptation degree of electrical parameters between adjacent intelligent components; the preset threshold is a standard value for judging the matching degree between components; the target adjacent intelligent component refers to a device that is confirmed to be safely connected after matching degree verification.

[0048] Specifically, this step performs electrical parameter matching analysis on each pair of adjacent intelligent components. First, the rated current ratio of the two components is calculated, such as the upstream component rated current 400A and the downstream component rated current 300A, then the ratio is 0.75. Then check the compatibility of the interface type, including the number of phases, voltage level, wiring method and other parameters, and quantify the matching of each parameter as a value between 0 and 1. The current ratio and interface parameter matching degree are weighted calculated according to the ratio of 6:4 to get the comprehensive matching degree. When the comprehensive matching degree exceeds the preset threshold (such as 0.85), it is confirmed that this pair of adjacent components can be safely connected, and they are marked as target adjacent intelligent components. Through this screening, the device combinations that are not suitable for connection are excluded, ensuring that the connection relationship in the topology structure meets the electrical safety requirements.

[0049] Step 203: Establishing the series and parallel connection relationship between the target adjacent intelligent components with the weighted value of real-time working current and temperature as the edge weight, and generating a first topology structure.

[0050] Wherein, the edge weight represents the importance index of the connection line in the topology structure; the series and parallel connection relationship refers to the electrical connection mode between the intelligent components; and the first topology structure is a network model reflecting the actual connection state of the current power distribution system.

[0051] Specifically, this step constructs the topology structure model of the power distribution system. First, the real-time working current and temperature of each target adjacent intelligent component are normalized, i.e., the current value is divided by the rated current to obtain the load rate, and the temperature value is converted into a percentage of the rated working temperature of the device. The edge weight value of the connection relationship is obtained by multiplying the normalized current value and temperature value by the weights of 0.7 and 0.3 respectively and summing them up. According to the physical connection mode of the device, the series and parallel relationship is identified. For example, when multiple circuit breakers are connected to the same bus, it is a parallel relationship, and when the circuit breakers are connected in series to form a power supply branch, it is a series relationship. All target adjacent intelligent components are connected according to the actual connection mode, and the edge weight value calculated is marked on each connection relationship, and finally a complete first topology structure is generated. The topology structure is expressed in a graphical manner, wherein the nodes represent the intelligent components, the lines represent the connection relationship between the devices, and the weight value on the line reflects the load level and operating state of the connection.

[0052] Step 102: Sending a detection signal to each topology node in the first topology structure, and determining any topology node as a target node that has undergone topology change when no feedback signal from any topology node is received for a continuous preset number of times.

[0053] Wherein, the topology node represents the intelligent component connection point in the topology structure of the power distribution system; the detection signal refers to a data packet used to verify the communication state of the node; the feedback signal represents the response data returned by the node after receiving the detection signal; the preset number of times refers to the threshold number of consecutive detection failures set by the system; and the target node refers to the specific node position that has undergone topology change.

[0054] Specifically, after the first topology structure is constructed, the system needs to monitor the change of the topology structure in real time. This step realizes the monitoring of topology change by periodically sending detection signals to all nodes in the topology structure. The system sends detection data packets containing node identification, time stamp and check code to each topology node at a predefined time interval (such as 100 ms). A normally working node will return a feedback signal with state information within a specified time (such as 50 ms) after receiving the detection signal. When a node fails to return an effective feedback signal for a plurality of times (such as a preset number of 3 times), the system determines that the node has changed topology and marks it as a target node for subsequent topology reconstruction and risk assessment.

[0055] In some embodiments, the detection of topology node change can be realized in various ways: optionally, a communication link based on TCP / IP protocol is first constructed, and a heartbeat detection packet is sent to each node; then a timer is used to monitor the response time of each node; finally, the node state is determined according to the number of response timeouts, and the node is confirmed to have changed when the number of timeouts reaches a preset value. Optionally, a master-slave polling mechanism is used, and the power distribution master station is used as the master device to periodically poll the state of each slave device node; then the response results of each node are recorded in a state table; finally, the continuously unresponsive records in the state table are analyzed to identify the target node that has changed. It can be understood that other communication protocols and detection mechanisms can also be used to realize the monitoring of topology node change, which is not limited here.

[0056] Step 103: dynamically reconstructing the first topology structure according to the topology change type of the target node at a plurality of logical layers to obtain a second topology structure, the logical layers including a micro layer, a meso layer and a macro layer.

[0057] Wherein, the topology change type represents the specific form of the change of the target node, including node failure, node replacement or node addition, etc.; the micro layer refers to a logical layer with a single intelligent component as a basic unit; the meso layer represents a logical layer with a loop in the power distribution cabinet as a unit; the macro layer refers to a logical layer with the entire power distribution system as an object; and the second topology structure represents the reconstructed power distribution system network model.

[0058] Specifically, when the target node is detected to have a topology change, the system needs to adjust the power distribution network structure. This step first identifies the topology change type of the target node, such as node failure caused by device failure, parameter change caused by device replacement, node expansion caused by new device addition, etc. Then, according to the change type, the corresponding structure reconstruction is performed at different logical levels. For the microscopic layer, the connection relationship and working parameters of a single intelligent component are adjusted; for the mesoscopic layer, the loop structure and load distribution in the power distribution cabinet are reconstructed; for the macroscopic layer, 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 reflecting the current actual running state, which contains complete device connection relationship, loop configuration and system-level topology information.

[0059] In some embodiments, dynamic reconstruction of the topology structure can be achieved in various ways: optionally, first analyze the change characteristics of the target node and determine the affected logical level; then update the connection relationship between devices by modifying the connection table of the micro component at the micro level; then recalculate the load distribution of the loop and adjust the protection setting value at the mesoscopic level; finally, optimize the power supply scheme of the system and update the global topology model at the macro level. Optionally, a multi-level topology reconstruction model is constructed based on graph theory algorithm; then an independent reconstruction rule set is established for each logical layer; finally, the reconstruction operation of each layer is performed in turn in a bottom-up manner, and a data synchronization mechanism is established between layers. It can be understood that other algorithms and strategies can also be used to realize multi-level topology reconstruction, which are not limited here.

[0060] Based on the above embodiments, as an optional embodiment, in step 103: according to the topology change type of the target node, the first topology structure is dynamically reconstructed at multiple logical levels, and this step can further include the following steps:

[0061] Step 301: Construct a logical layer system of a micro layer with intelligent components as nodes, a mesoscopic layer with loops in the power distribution cabinet as units, and a macro layer with the power distribution system as a whole.

[0062] Among them, the logical layer system represents a hierarchical structure model divided according to different granularities; the micro layer node refers to the smallest topology unit representing a single intelligent component; the loop in the power distribution cabinet refers to a power supply circuit composed of multiple intelligent components; and the power distribution system as a whole contains the complete network structure of all power distribution cabinets and lines.

[0063] Specifically, this step constructs a three-layer logical structure model of the power distribution system. At the micro level, each intelligent component (such as a circuit breaker, switch, and meter) is treated as an independent node, and its working parameters, connection ports, and communication addresses are recorded. At the meso level, the intelligent components within a power distribution cabinet are combined into a power supply loop unit, and the topology of the loop, protection configuration, and load distribution are recorded. At the macro level, each power distribution cabinet is treated as a node, and the network structure of the entire power distribution system is constructed with feeder connections as edges, and the power supply topology, load partitioning, and tie-in relationships at the system level are recorded. Through this hierarchical modeling approach, multi-scale descriptions of the power distribution system from components to systems are achieved, providing a structured processing framework for subsequent topology reconstruction.

[0064] Step 302: In the logical layer system, when the target node corresponds to a change in the intelligent component, adjust the connection relationship of the intelligent component with the upstream input node and downstream output node at the micro level, and keep the meso level and macro level unchanged.

[0065] Wherein, the upstream input node refers to the power supply side device for the target intelligent component; the downstream output node refers to the load side device powered by the target intelligent component; the connection relationship includes physical connection mode and electrical parameter configuration.

[0066] Specifically, this step handles topology changes at the intelligent component level. When a fault, replacement, or parameter change occurs in a certain intelligent component, first locate the upstream and downstream connection devices of the component at the micro level. For upstream devices, update the connection state and output parameters of their output ports; for downstream devices, adjust the connection configuration and protection setting values of their input ports. For example, when a 400A circuit breaker is replaced with a 630A new device, the connection parameters of the new device with the upstream bus and downstream load need to be modified. These adjustments only affect the connection relationship of the devices at the micro level and do not affect the overall structure of the power distribution cabinet loop (meso level) and the power supply topology at the system level (macro level). Through this local adjustment method, precise response to individual device changes is achieved while maintaining stable operation of other parts of the system.

[0067] Step 303: When any loop in the target node corresponding power distribution cabinet fails, delete the series or parallel logic of the intelligent components in the fault loop at the meso level, and simultaneously update the external connection state of the power distribution cabinet at the macro level.

[0068] Wherein, loop failure indicates that a circuit within the power distribution cabinet has a circuit break, short circuit, or overload, etc. Series logic refers to the power supply path formed by connecting the intelligent components end-to-end; parallel logic represents the parallel connection of multiple intelligent components; external connection state refers to the electrical connection relationship between the power distribution cabinet and other devices.

[0069] Specifically, this step handles the fault of the loop level in the power distribution cabinet. When a fault occurs in a loop in the power distribution cabinet, the system first identifies all intelligent components contained in the faulty loop. The series or parallel connection relationship between these components is deleted in the mesoscopic layer, and the connection between the faulty loop and the busbar of the power distribution cabinet is disconnected. For example, when a short-circuit fault occurs in a three-phase loop, the connection of all circuit breakers and switches in the loop needs to be disconnected, and the busbar load distribution needs to be updated. At the same time, the fault information is transmitted upward to the macroscopic layer, and the external connection parameters of the power distribution cabinet, including the available capacity, load rate, and protection range, are modified. This cross-layer linkage processing method ensures the timely synchronization of fault information between different levels and avoids the expansion of local faults to the entire system.

[0070] Step 304: When the target node corresponds to an inter-cabinet feeder line fault, reconstruct the feeder connection logic in the macroscopic layer, synchronize it downward to the mesoscopic layer, and mark the loop state of the power distribution cabinet as offline.

[0071] Among them, the inter-cabinet feeder line refers to the power supply line connecting different power distribution cabinets; the feeder connection logic represents the power supply relationship and communication link between power distribution cabinets; the loop state refers to the working state of each power supply loop in the power distribution cabinet.

[0072] Specifically, this step handles the case of feeder line fault between power distribution cabinets. When a feeder line connecting two power distribution cabinets fails, the affected power supply path is first identified in the macroscopic layer. The system-level power supply scheme is re-planned, including adjusting the feeder connection relationship, re-distributing the load, and updating the protection configuration. These changes are transmitted downward to the mesoscopic layer through the inter-layer data synchronization mechanism, updating the internal loop configuration of the related power distribution cabinet. For the power distribution cabinet that loses power, all its loops are marked as offline, and the emergency power supply scheme is triggered. For example, when a main feeder line fails, the system will enable the standby feeder line or ring network power supply scheme, and adjust the operating parameters of each power distribution cabinet accordingly. Through this top-down collaborative processing, the rapid response and orderly recovery of system-level faults are achieved.

[0073] Step 104: Perform electrical characteristic verification on any closed loop in the second topology.

[0074] Among them, the closed loop represents the line part that forms a complete current path in the power distribution system, including the power supply, load, and connecting conductor; the electrical characteristic verification is the process of verifying the voltage and current characteristics in the circuit; the line segment represents the conductor part between two nodes in the closed loop; the voltage drop vector represents the size and direction of the voltage drop on the line; the branch current vector represents the size and direction of the current flowing through the line; the verification pass is used to indicate that the circuit meets the basic requirements of Kirchhoff's law.

[0075] Specifically, the step is executed after the second topology structure is obtained by completing the topology reconfiguration, and is used for verifying whether the reconfigured topology structure meets the electrical characteristic requirement. First, all closed loops and node information thereof are extracted from the second topology structure, and then two-dimensional verification is performed on each closed loop: one is to calculate a voltage drop vector based on current values of each line section, and to verify whether a sum of the voltage drop vectors in the loop is zero, so as to ensure that the Kirchhoff's voltage law is met; and the other is to collect all branch current vectors flowing into and out of each node in the loop, and to verify whether a sum of the current vectors is zero, so as to ensure that the Kirchhoff's current law is met. Only when both the verifications pass, can it be determined that the electrical characteristic verification of the closed loop passes.

[0076] In some embodiments, the electrical characteristic verification of the closed loop can be implemented in various ways: optionally, a depth-first search algorithm is used to identify the closed loop, real-time current values are collected by the smart meter for each line section, voltage drops are obtained by multiplying the current values and line impedances, voltage drop vector directions are determined by considering current directions, all voltage drop vectors are accumulated to verify whether the sum is zero, and current balance equations are established at each node, with inflow currents being positive and outflow currents being negative, to verify whether the equations are established; optionally, a network connectivity graph is constructed based on a graph theory method, all closed loops are found out by using a minimum loop search algorithm, active power and reactive power of each branch are collected by the power analyzer, complex form voltage drops and current values are calculated, and whether the node current conservation and the loop voltage balance law are met is verified respectively. It can be understood that other electrical characteristic verification methods can also be used to verify the closed loop, which is not limited here.

[0077] On the basis of the above embodiments, as an optional embodiment, in step 104, the electrical characteristic verification is performed on any closed loop in the second topology structure, and the step can further include the following steps:

[0078] Step 401: Extracting any closed loop in the second topology structure and nodes in the closed loop.

[0079] The closed loop represents a circuit part forming a complete path in the power distribution system; the node represents a connection point in the power distribution system, including a component terminal, a line intersection point, etc.; and the extraction operation refers to a process of identifying and obtaining a specific circuit element from the topology structure.

[0080] Specifically, the extraction process of the closed loop and the node in the second topology structure specifically includes the following contents: first, the depth-first search algorithm is used to traverse the connection relationship of the second topology structure, starting from the starting node, searching forward along the connection relationship until returning to the starting node, that is, a complete closed loop can be identified. In the search process, all the nodes and connection lines passed are recorded, and these information is saved to the closed loop data structure. For example, for a simple power distribution loop, it may contain breaker node A, bus node B, load switch node C, etc. Through the search, a closed loop A-B-C-A can be obtained. At the same time, the information of each node in the loop is extracted and saved separately, including node type, position coordinates, connection relationship and other attributes. In this way, the complete closed loop topology structure and node distribution can be obtained.

[0081] Step 402: 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 whether the sum of each voltage drop vector is zero is verified.

[0082] Wherein, the current flowing through represents the actual current value through the line; the voltage drop vector represents the size and direction of the voltage drop on the line; the line segment represents the wire part between two adjacent nodes in the closed loop.

[0083] Specifically, the verification process of the electrical characteristics of each line segment in the closed loop specifically includes: first, install an intelligent current detection device on each line segment to collect real-time current values. When collecting, high-precision current transformers are used for measurement, and the sampling frequency is not less than 1 kHz to ensure data accuracy. Then, the voltage drop vector is calculated based on the obtained current value. When calculating, the resistance value of the line segment is multiplied by the current flowing through to obtain the voltage drop size, 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 is 100A, the voltage drop is 10V; if the current flows from A to B, the voltage drop direction is from A to B. After similar calculation is performed on all line segments in the closed loop, vector addition operation is performed on all voltage drop vectors. 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 judged whether the reconstructed topology structure meets the basic electrical characteristic requirements.

[0084] Step 403: For the node, the branch current vector flowing into and out of the node is collected, and whether the sum of the branch current vector is zero is verified.

[0085] Wherein, the branch current vector represents the current size and direction flowing through each connection branch of the node; the inflow current represents the current entering the node; the outflow current represents the current leaving the node; the current vector sum represents the algebraic sum of all currents at the node.

[0086] Specifically, the current characteristic verification process for the node is as follows: first, install a current sensor on each connection branch of the node to collect real-time current data. High-precision Hall effect current sensors are used for collection, with a sampling rate of 1 kHz or higher 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, a certain distribution cabinet connection point has three branches: branch 1 has a flow-in current of 50A, branch 2 has a flow-in current of 30A, and branch 3 has a flow-out current of 80A, which is represented as +50A, +30A, and -80A. Then, the vector superposition calculation is performed on all branch currents, and the algebraic sum of all flow-in and flow-out currents is added. According to Kirchhoff's current law, the algebraic sum of currents at the node should be zero, which reflects the physical nature of charge conservation. In this way, the current balance state at the node can be verified.

[0087] Step 404: If the sum of the voltage drop vectors and the sum of the branch current vectors are zero, it is determined that the electrical characteristic verification is passed.

[0088] Wherein, the electrical characteristic verification passed indicates that the topology structure satisfies the basic law of circuit; the sum of voltage drop vectors indicates the vector sum of all voltage drops in the closed loop; the sum of branch current vectors indicates the vector sum of all currents at the node.

[0089] Specifically, the specific process of determining whether the electrical characteristic verification is passed is as follows: first, check the voltage characteristics of the closed loop, and perform accurate vector addition operation on the previously calculated voltage drop vectors of each line segment. When calculating, the size and direction of the voltage drop are considered, and the voltage drop vector is represented using complex numbers, and the real part and imaginary part of all voltage drops are added respectively. For example, if the voltage drops of three line segments are 5∠30°V, 8∠150°V and 10∠270°V, the sum of these complex numbers should be zero. At the same time, check the current characteristics of each node, and add all the current vectors flowing into and out of the node, also using complex numbers for calculation. Only when the sum of the voltage drop vectors in the closed loop and the sum of the branch current vectors at each node are both zero, can it be confirmed that the topology structure satisfies the electrical characteristic requirement, and the verification is passed. This double verification ensures that the reconstructed topology structure not only satisfies the voltage balance but also satisfies the current conservation.

[0090] Step 105: When the electrical characteristic verification is passed, based on the association relationship in the second topology structure, 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 topology 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 running parameters of the intelligent components in the topology influence range.

[0091] The association relationship represents the electrical and logical connection mode between devices in the power distribution system; the power distribution cabinet connection relationship refers to the power supply path and feeder connection mode between power distribution cabinets; the component association link represents the series and parallel connection sequence between intelligent components; the topology influence range refers to a set of all devices and lines affected by the change of the target node; the historical operation parameters refer to the current, temperature, state and other data recorded by the intelligent components during past operation; and the risk level is used to represent the influence degree of the topology change on the system operation safety.

[0092] Specifically, when the second topology structure satisfies the electrical characteristic verification, i.e., the Kirchhoff's law verification, the system needs to evaluate the influence range and risk level of the topology change. This step first starts from the target node, traces upstream to the power supply side along the power supply direction, determines the affected power distribution cabinet connection path, and at the same time extends downstream to the load side to identify the associated intelligent component link. In the determined influence range, the historical operation data of each device are extracted, including the load rate, temperature change trend, fault frequency, etc. According to these parameters, risk indicators such as device overload probability and fault diffusion possibility are calculated. Through comprehensive analysis of the influence range and historical data, the system operation risk brought by the topology change is quantitatively evaluated.

[0093] In some embodiments, the evaluation of the topology influence range and risk level can be realized in various ways: optionally, a breadth-first search algorithm is first used to expand the search of affected devices from the target node upstream and downstream; then an influence propagation model is established based on the electrical coupling relationship between devices; then the historical operation data of devices in the influence range are extracted and standardized; and finally a weighted scoring method is used to calculate the comprehensive risk level. Optionally, a risk evaluation model based on a graph neural network is constructed; then the device historical data are converted into feature vectors in time sequence and input into the model; then the influence relationship between devices is calculated through node embedding; and finally the risk level threshold is determined based on the model output result. It can be understood that other data analysis and machine learning methods can also be used to realize the evaluation of the topology influence and risk, which is not limited here.

[0094] On the basis of the above-mentioned embodiments, as an optional embodiment, in step 105, based on the association relationship in the second topology structure, the power distribution cabinet connection relationship is determined in the upstream direction from the target node, and the component association link is determined in the downstream direction, and the topology influence range of the power distribution cabinet connection relationship and the component association link is determined. This step can further include the following steps:

[0095] Step 501: Obtain the identification of the inlet circuit breaker of the power distribution cabinet where the target node is located, and trace upwards step by step based on the inlet circuit breaker identification until the total power distribution cabinet to obtain the power distribution cabinet connection relationship.

[0096] Wherein, the entry breaker identification represents a unique identification code of the main incoming line breaker of the power distribution cabinet; the total power distribution cabinet refers to the most upper level power distribution equipment in the power supply system; the power distribution cabinet connection relationship refers to the power supply path and hierarchical relationship between the power distribution cabinets.

[0097] Specifically, it is necessary to determine the complete power supply path from the power distribution cabinet where the target node is located to the total power distribution cabinet. The system first locates the entry breaker of the power distribution cabinet where the target node is located from the target node, and obtains the identification information of the breaker. Then, based on the topology database of the power distribution system, the upper level power distribution cabinet of the entry breaker is queried. This process is repeated to trace back the entry breaker of each power distribution cabinet level by level until the total power distribution cabinet is found. In the tracing process, the connection mode between each level of power distribution cabinet is recorded, including feeder number, breaker capacity and protection configuration, etc., and finally a complete power supply link from the total power distribution cabinet to the target power distribution cabinet is formed.

[0098] In some embodiments, the tracing of the power distribution cabinet connection relationship can be realized in various ways: optionally, a power distribution cabinet index based on a hash table is first established, and the entry breaker identification is taken as the key value; then the corresponding upper level power distribution cabinet information is queried through the breaker identification; then the queried power distribution cabinet information is pushed into 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 power distribution cabinet in the data structure is located based on the entry breaker identification; then the parent node is found upwards by using a recursive method; finally, the search path is converted into a connection relationship sequence. It can be understood that other data structures and query algorithms can also be used to realize the tracing of the power distribution cabinet connection relationship, which is not limited here.

[0099] Step 502: based on the output port identification of the target node, determine the downstream intelligent component connected with the target node, recursively obtain the load branch of the downstream intelligent component, and generate a component association link.

[0100] Wherein, the output port identification 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 load device; the component association link represents the connection sequence and hierarchical relationship between the intelligent components.

[0101] Specifically, it is necessary to establish a complete power supply path from the target node to the final load. The system first reads the output port information of the target node, and queries the downstream device directly connected with it in the topology database according to the port identification. For each downstream device, the output port connected device is continuously queried, and the recursive method is used to continuously extend downwards until the terminal load is reached. In the search process, the type, capacity and protection parameters of each intelligent component, as well as the connection mode and electrical parameters between the devices are recorded, and finally a hierarchical link structure containing all associated devices is generated.

[0102] In some embodiments, the generation of the component device association link can be achieved in various ways: optionally, first, 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 device found in the search are recorded; finally, the search results are sorted into a tree-shaped association link. Optionally, a relational database of device ports is established; then, the downstream connected devices are recursively found through SQL query statements; next, the query results are converted into a hierarchical JSON data structure; finally, the complete association link is generated based on the data structure. It can be understood that other graph algorithms and data processing methods can also be used to achieve the generation of the component device association link, which is not limited here.

[0103] On the basis of the above-mentioned embodiments, as an optional embodiment, in step 502: based on the output port identifier of the target node, the downstream intelligent component device connected to the target node is determined, the load branch of the downstream intelligent component device is recursively obtained, and the component device association link is generated. This step can also include the following steps:

[0104] Step 512: traverse the output port identifier of the target node to obtain the downstream intelligent component device corresponding to the output port identifier; extract the load type and load capacity of the downstream intelligent component device, and establish the corresponding component device cascade table.

[0105] Wherein, the output port identifier represents the interface number of the intelligent component device to supply power to the downstream; the downstream intelligent component device refers to the device directly receiving power supply from the target node; the load type represents the functional category of the electric device; the load capacity refers to the rated power or current value of the device; and the component device cascade table refers to a data structure recording the power supply relationship between devices.

[0106] Specifically, this step constructs a connection relationship table from the target node to the directly downstream device. The system reads all output port identifiers of the target node in order, and queries the downstream device information connected to each port through the topology database. For each downstream intelligent component device, its technical parameters are extracted, including device type (such as circuit breaker, distribution box, transformer, etc.), rated current, rated power, and other load characteristic data. These information is organized into a standardized data structure to establish a component device cascade table. The table contains fields such as port identifier, device identifier, load parameter, etc., which are used to record the direct connection relationship and technical characteristics between devices.

[0107] Step 522: based on the component device cascade table, recursively traverse the downstream intelligent component device until the terminal load device is reached; sort the recursively traversed downstream intelligent component devices 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 device association link.

[0108] Wherein, the recursive traversal refers to repeatedly performing the process of finding down devices; the terminal load device refers to the load device at the end of the power supply link; the load branch refers to the complete power supply path from the target node to the terminal load; the tree structure refers to a hierarchical data organization form; and the component associated link refers to the power supply sequence and hierarchical relationship between devices.

[0109] Specifically, this step constructs the complete power supply link structure in a recursive manner. The system, based on the component cascade table, repeatedly performs the downstream device finding process starting from each directly downstream device. For each found device, the next level device connected to its output port is queried, until the terminal load that is no longer powered down is found. All found devices are sorted according to the power supply sequence to form the complete power supply path from the source end to the load end. Then a tree data structure is created, the target node is set as the root node, and the hierarchical devices establish parent-child relationship according to the power supply sequence, and finally the complete component associated link is generated.

[0110] Step 503: According to the switchboard connection relationship and the component associated link, mark the upstream influence switchboard and the downstream influence intelligent component of the topology change of the target node.

[0111] Wherein, the upstream influence switchboard refers to the switchboard affected by the topology change on the power supply path from the target node to the general switchboard; the downstream influence intelligent component refers to the device affected by the topology change on the power supply path from the target node to the terminal load; and the topology change refers to the change of the connection relationship, running state or technical parameter of the target node.

[0112] Specifically, the influence device of the topology change needs to be determined based on the connection relationship obtained in the previous step. The system first analyzes the topology change type of the target node, including connection relationship change, running parameter adjustment or state switching, etc. In the upstream direction, the switchboard connection relationship is traversed to identify the switchboard that has electrical or protection cooperation relationship with the target node, and these switchboards are marked as upstream influence devices. In the downstream direction, the component associated link is traversed to identify the intelligent component whose load distribution or protection configuration is changed due to the change of the target node, and these devices are marked as downstream influence devices. Through this bidirectional analysis method, the influence range of the topology change is completely marked.

[0113] In some embodiments, the marking of the affected devices can be achieved in various ways. Optionally, an electrical distance-based influence attenuation model is first established; then the electrical distances of the devices to the target node are calculated; the influence degree is then determined according to the distance values and the coupling relationship between the devices; and finally, the devices with an influence degree exceeding a threshold are marked as affected devices. Optionally, an association degree matrix between the devices is constructed; the influence transmission coefficients between the devices are then calculated through matrix operations; the diffusion range of the influence is then determined based on the influence coefficients; and finally, the devices within the diffusion range are marked as affected devices. It can be understood that other mathematical models and analysis methods can also be used to mark the affected devices, which are not limited herein.

[0114] Step 504: Determine the coverage range of the upstream influence power distribution cabinet and the downstream influence smart component as the topological influence range.

[0115] The coverage range refers to the device set composed of the upstream influence power distribution cabinet and the downstream influence smart component; and the topological influence range represents the spatial range of all devices and lines affected by the topology change of the target node.

[0116] Specifically, the marked affected devices need to be integrated into a complete influence range. The system first collects all the marked affected upstream power distribution cabinets, determines the physical distribution range and electrical connection boundary of these power distribution cabinets. At the same time, all the marked affected downstream smart components are sorted out, and the installation location and power supply range of these devices are determined. The distribution range of the upstream power distribution cabinet and the power supply range of the downstream smart component are combined to obtain a complete spatial range, which contains all the devices and lines affected by the topology change of the target node.

[0117] In some embodiments, the determination of the influence range can be achieved in various ways. Optionally, a device distribution map based on a geographic information system is first constructed; the affected devices are then marked on the distribution map; the spatial boundary of these devices is then calculated using a convex hull algorithm; and finally, the area within the boundary is determined as the influence range. Optionally, a hierarchical regional division model of the devices is established; the affected devices are then mapped to the corresponding regional units; the regional units containing the affected devices are then merged; and finally, the complete influence range boundary is generated. It can be understood that other spatial analysis and regional division methods can also be used to determine the influence range, which are not limited herein.

[0118] On the basis of the above embodiments, as an optional embodiment, in step 105: the risk level is calculated in combination with the historical operation parameters of the smart components within the topological influence range. This step can further include the following steps:

[0119] Step 505: Collect the historical operating parameters of each intelligent component in the topology influence range within a preset time period, including the number of overloads, load type, rated power and real-time current; determine the importance weight of each intelligent component in the topology influence range according to the load type.

[0120] wherein the historical operating parameters represent various types of operating data recorded by the intelligent component during past operation; the number of overloads refers to the cumulative number of times that the device exceeds the rated load operation; the load type represents the functional classification of the electrical equipment, such as fire protection, emergency lighting, important production equipment, etc.; the rated power refers to the nominal power value of the device under normal operation; the real-time current represents the load current of the device currently running; the importance weight represents the relative importance of the device in the system, usually represented by a value between 0 and 1.

[0121] Specifically, this step is performed after determining the topology influence range, for collecting device operating data and determining the weight. The system first sets the time window for data collection, such as the last 30 days or 90 days, and extracts the operating records of all intelligent components in the influence range within that period from the database. For each device, the number of overload operations is counted, the load type information of the device is recorded, the rated power parameter is extracted, and the current real-time current value is collected. Then, according to the pre-defined load type importance classification standard, weight coefficients are assigned to different types of loads. For example, the weight of fire protection equipment is 1.0, the weight of emergency lighting is 0.9, the weight of important production equipment is 0.8, and the weight of ordinary lighting is 0.6, etc. In this way, a quantitative index system reflecting the importance of the device is established.

[0122] Step 506: Calculate the ratio of the real-time current of each intelligent component to the rated current to obtain the current load rate.

[0123] wherein the real-time current represents the current value of the intelligent component at the current operating time, in amperes (A); the rated current is the maximum current value that the device is allowed to pass continuously under normal working conditions, in amperes (A); the current load rate represents the percentage of the actual operating current to the rated current, represented in decimal form; the intelligent component refers to a power distribution device with electrical parameter measurement and communication function.

[0124] Specifically, this step is executed after obtaining the device operating parameters, and is used to calculate the load level of the device. The system first reads the current value of each intelligent component from the real-time database, in amperes. At the same time, the rated current parameters of each device are extracted from the device account database. For each intelligent component, the real-time current value is divided by the rated current value to obtain a ratio between 0 and 1, i.e. the current load rate. For example, the rated current of a certain circuit breaker is 100 A, and the current real-time current is 75 A, so the current load rate is 0.75. The load rate reflects the actual load level of the device, and is an important indicator for evaluating the operating state of the device.

[0125] Step 507: Based on the importance weight, current load rate and overload times of each intelligent component, a weighted calculation is performed to obtain a risk level score corresponding to the intelligent component; for each intelligent component, the corresponding risk level is divided according to the numerical range of the risk level score.

[0126] Among them, the importance weight represents the relative importance of the device in the system, and the value range is 0 to 1; the current load rate represents the ratio of real-time current to rated current; the overload times refers to the cumulative number of times the device exceeds the rated load operation; the risk level score refers to the risk quantification index obtained by weighted calculation; the risk level represents the classification result of the device operating safety state, which is usually divided into three levels of low risk, medium risk and high risk.

[0127] Specifically, this step is executed after obtaining the parameters of the device to perform risk assessment calculation. The system uses the weighted summation method to calculate the risk level score, and the calculation formula is: risk level score = W1 x importance weight + W2 x current load rate + W3 x (overload times / reference value), wherein W1, W2, W3 are the weight coefficients of each index, and the reference value is a standardization factor. For example, the importance weight of a certain circuit breaker is 0.9, the current load rate is 0.75, the overload times is 5, the reference value is 10, and the weight coefficients are 0.4, 0.3 and 0.3 respectively, then the risk level score is: 0.4 x 0.9 + 0.3 x 0.75 + 0.3 x (5 / 10) = 0.36 + 0.225 + 0.15 = 0.735. Then, according to the preset score interval, the risk level is divided: the score less than 0.6 is low risk, the score between 0.6 and 0.8 is medium risk, and the score greater than 0.8 is high risk.

[0128] In some embodiments, the risk level assessment can be achieved in various ways: optionally, first, an evaluation model based on fuzzy mathematics is constructed; then the indicators are subjected to fuzzy quantitative processing; then the risk membership degree is calculated through fuzzy comprehensive evaluation; finally, the risk level is determined according to the maximum membership degree principle. Optionally, a risk assessment system based on the analytic hierarchy process is established; then the index weight is determined by expert scoring method; then the standardized score of each index is calculated; finally, the comprehensive risk value is obtained by weighted summation. It can be understood that other mathematical models and evaluation methods can also be used to realize the assessment of risk level, which is not limited here.

[0129] Step 106: generating a corresponding temporary coping strategy according to the risk level; based on the topological influence range, the risk level and the temporary coping strategy, generating a risk assessment report of the power distribution system.

[0130] Among them, the temporary coping strategy means the short-term prevention and control measures formulated for the current risk situation; the risk level refers to the quantitative evaluation result of system operation safety; the topological influence range refers to the set of devices and lines affected by the change of system structure; the risk assessment report refers to the comprehensive analysis document of system safety state, including risk description, influence range, coping measures and other contents.

[0131] Specifically, after the risk level calculation is completed, corresponding prevention and control measures need to be formulated and a complete evaluation report needs to be formed. This step first selects the matching prevention and control measures from the pre-set coping strategy library according to the calculated risk level, including device monitoring frequency adjustment, protection setting value modification, operation mode optimization and other specific measures. Then, the structured risk assessment report is generated by comprehensively analyzing the device state in the topological influence range, the quantitative indicators of the risk level and the formulated coping strategy. The report records the risk source identification result, the influence range analysis, the risk level determination basis, the coping measure content and the execution suggestion and other information in detail, providing decision support for operation and maintenance personnel.

[0132] In some embodiments, the generation of the risk assessment report can be achieved in various ways: optionally, first, a strategy matching rule library based on risk level is constructed; then the corresponding coping strategy set is extracted according to the current risk level; then the applicability of the strategy is evaluated in combination with the topological influence range; finally, the evaluation report document is generated in accordance with the standardized template format. Optionally, an expert knowledge base system for risk assessment is established; then the risk level and the influence range are taken as input parameters; then the targeted coping suggestions are generated through knowledge reasoning; finally, the evaluation process and results are integrated into a complete report. It can be understood that other decision support and document generation methods can also be used to realize the production of risk assessment report, which is not limited here.

[0133] Reference Figure 2The application provides a risk assessment system for dynamic reconfiguration of a power distribution topology, comprising a data acquisition module, a dynamic reconfiguration module, a risk level determination module and a risk assessment module.

[0134] The data acquisition module is configured to acquire real-time state data of intelligent components in the power distribution system and construct a first topology of the power distribution system based on the real-time state data.

[0135] The dynamic reconfiguration module is configured to send a detection signal to each topology node in the first topology, determine any topology node as a target node that has undergone topology change when no feedback signal from any topology node is received for a preset number of consecutive times, and dynamically reconfigure the first topology at multiple logical layers according to the topology change type of the target node to obtain a second topology, wherein the logical layers include a microscopic layer, a mesoscopic layer and a macroscopic layer.

[0136] The risk level determination module is configured to perform electrical characteristic verification on any closed loop in the second topology, determine power distribution cabinet connection relationships in an upstream direction and component associated links in a downstream direction based on the associated relationships in the second topology when the electrical characteristic verification is passed, determine a topology influence range of the power distribution cabinet connection relationships and the component associated links, and calculate a risk level in combination with historical operation parameters of intelligent components in the topology influence range.

[0137] The risk assessment module is configured to generate a corresponding temporary response strategy according to the risk level, and generate a risk assessment report of the power distribution system based on the topology influence range, the risk level and the temporary response strategy.

[0138] On the basis of the above embodiment, the data acquisition module is further configured to acquire static data and dynamic data stored in a built-in topology protocol chip of each intelligent component, wherein the static data includes a component unique identifier, a rated current and an interface type, and the dynamic data includes an online state, a real-time working current and a temperature; the target intelligent component in a power-on state is identified according to the online state, and adjacent intelligent components of the target intelligent component are acquired based on the component unique identifier; for the adjacent intelligent components, when the matching degree of the rated current and the interface type is greater than a preset threshold, the adjacent intelligent components are determined as target adjacent intelligent components; the series-parallel connection relationships between the target adjacent intelligent components are established by taking the weighted values of the real-time working current and the temperature as edge weights, and the first topology is generated.

[0139] On the basis of the above-mentioned embodiments, the dynamic reconstruction module is further used to construct a micro layer taking the intelligent component as a node, a meso layer taking the loop in the power distribution cabinet as a unit, and a macro layer taking the power distribution system as a whole; in the logical layer system, when the target node corresponds to a change in the intelligent component, the connection relationship of the intelligent component with the upstream input node and the downstream output node is adjusted in the micro layer, and the meso layer and the macro layer remain unchanged; when any loop in the target node corresponds to a fault in the power distribution cabinet, the series or parallel logic of the intelligent component in the fault loop is deleted in the meso layer, and the external connection state of the power distribution cabinet in the macro layer is updated synchronously; when the target node corresponds to a fault in the feeder line between the power distribution cabinets, the feeder connection logic is reconstructed in the macro layer, and is synchronously transmitted downward to the meso layer, and the loop state of the power distribution cabinet is marked as offline.

[0140] On the basis of the above-mentioned embodiments, the risk level determination module is further used to extract any closed loop in the second topology structure and the nodes in the closed loop; for the closed loop, the current flowing through each line section is collected, and the voltage drop vector of each line section is calculated based on the current flowing through each line section, and it is verified whether the sum of each voltage drop vector is zero; for the node, each branch current vector flowing into and out of the node is collected, and it is verified whether the sum of each branch current vector is zero; if the sum of each voltage drop vector and the sum of each branch current vector are zero, it is determined that the electrical characteristic verification is passed.

[0141] On the basis of the above-mentioned embodiments, the risk level determination module is further used to obtain the inlet circuit breaker identifier of the power distribution cabinet where the target node is located, trace upwards based on the inlet circuit breaker identifier until the total power distribution cabinet to obtain the power distribution cabinet connection relationship; based on the output port identifier of the target node, determine the downstream intelligent component connected with the target node, recursively obtain the load branch of the downstream intelligent component, and generate the component associated link; according to the power distribution cabinet connection relationship and the component associated link, mark the upstream influence power distribution cabinet and the downstream influence intelligent component of the topology change of the target node; the coverage range of the upstream influence power distribution cabinet and the downstream influence intelligent component is determined as the topology influence range.

[0142] On the basis of the above-mentioned embodiments, the risk level determination module is further used to traverse the output port identifier of the target node, and obtain the downstream intelligent component corresponding to the output port identifier; extract the load type and load capacity of the downstream intelligent component, and establish a corresponding component cascade table; based on the component cascade table, recursively traverse the downstream intelligent component until the terminal load device is reached; sort each downstream intelligent component recursively traversed according to the power supply order to obtain a plurality of load branches, and map each load branch to a preset tree structure to obtain a component associated link.

[0143] On the basis of the above-mentioned embodiments, the risk level determination module is further configured to collect historical operation parameters of each intelligent component in the topology influence range within a preset time period, the historical operation parameters including overload times, load types, rated power and real-time current; determine the importance weight of each intelligent component in the topology influence range according to the load type; calculate the ratio of the real-time current of each intelligent component to the rated current to obtain a current load rate; perform weighted calculation based on the importance weight, the current load rate and the overload times of each intelligent component to obtain a risk level score of the corresponding intelligent component; and divide the risk level of each intelligent component according to the numerical range of the risk level score.

[0144] It should be noted that: the device provided in the above-mentioned embodiments is only exemplified by the division of the above-mentioned functional modules when realizing its functions, and in actual application, the above-mentioned functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the device and method embodiments provided in the above-mentioned embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0145] The present application also discloses an electronic device. Referring to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiments of the present application. The electronic device 300 can 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.

[0146] The communication bus 302 is configured to realize the connection and communication between the components.

[0147] The user interface 303 can include a display interface and a camera interface. Optionally, the user interface 303 can further include a standard wired interface and a wireless interface.

[0148] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface).

[0149] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interface graphs, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0150] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can alternatively be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of a risk assessment method for dynamic reconfiguration of a power distribution topology.

[0151] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to invoke an application program stored in the memory 305 and storing a risk assessment method for dynamic reconfiguration of a power distribution topology, which, when executed by one or more processors 301, causes the electronic device 300 to perform the method of one or more of the above-described embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0152] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0153] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The division of units is merely a logical function division. There can be another division manner for actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical or other forms.

[0154] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0155] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be implemented in the form of hardware or software functional units.

[0156] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0157] The above are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the disclosure.

[0158] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and examples are only considered as exemplary.

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, then 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 each 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.

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