Intelligent power failure range calculation method and system based on SSD file

Abstract

The invention provides an intelligent power failure range calculation method and system based on an SSD file, and belongs to the field of power system automation and intelligent operation and maintaining.The intelligent power failure range calculation method comprises the steps that a substation SSD file and a main wiring diagram file are loaded, and data information is obtained; generating a dynamically updated power system network model; according to the selected to-be-overhauled equipment, automatically identifying other equipment which is connected with the selected secondary equipment and is possibly influenced, and performing electrical connectivity verification to obtain an electrical connectivity matrix; adopting an improved breadth-first search algorithm BFS to calculate a candidate power failure area, and applying a preset power failure rule set to perform logic verification to obtain an optimized power failure range; and mapping the optimized power failure range to a main wiring diagram to realize a visual effect and generate a detailed maintenance guidance document. By means of the method, the power failure range is intelligently calculated when the substation equipment is overhauled.

Description

Technical Field

[0001] This invention relates to the field of power system automation and intelligent operation and maintenance, specifically to a method and system for calculating intelligent power outage range based on SSD files. Background Technology

[0002] In power system operation and maintenance, especially during the overhaul of secondary equipment in substations, the decision of which primary equipment to shut down largely relies on the familiarity and experience of maintenance personnel with the substation equipment topology. However, when faced with complex equipment interconnections and real-time status changes, maintenance personnel often struggle to quickly and accurately determine the scope of the maintenance impact, potentially leading to unnecessary expansion of power outages or the omission of important equipment. Currently, methods for mapping secondary system equipment to primary system equipment have emerged to assist in determining the outage scope during maintenance and improve the safety management level of substations. For example, Chinese invention patent CN105512213A, "A Visual Display Method for Status Information of Primary and Secondary Equipment in Intelligent Substations Based on Configuration Files and G Language," discloses a method for mapping primary and secondary equipment. It automatically generates primary equipment main wiring diagrams and bay wiring diagrams based on intelligent substation configuration files. By associating with intelligent terminals, merging units, and protection devices, it organically connects and visually displays the primary and secondary equipment of the substation, expanding the application scope of configuration files. This graphically and intuitively displays the topological relationships and status information between primary and secondary equipment, facilitating equipment operation and maintenance and improving the safety management level of substations. While the inventions described above have played an auxiliary role in improving safety management, they lack associated maintenance instructions and verification of mapped electrical connectivity, thus making it impossible to intelligently calculate the power outage range. Summary of the Invention

[0003] The technical problem to be solved by this invention is how to intelligently calculate the power outage range and improve the level of safety management when overhauling secondary equipment in substations.

[0004] The present invention solves the above-mentioned technical problems through the following technical means: This invention provides a method for intelligent power outage range calculation based on SSD files, comprising the following steps: S1. Load the substation SSD file and main wiring diagram file to obtain the logical connection relationship between devices, actual installation location and physical connection information; S2. Based on the above information, various devices are abstracted as nodes in the topology model, and weighted edges are constructed for adjacent devices according to the actual electrical connection relationship to generate a dynamically updated power system network model. S3. Based on the selected secondary equipment to be inspected, automatically identify other equipment connected to the selected secondary equipment that may be affected, and expand these equipment to the queue of equipment to be analyzed. S4. Perform electrical connectivity verification on the above equipment queue to obtain the electrical connectivity matrix; S5. Based on the power system network model and electrical connectivity matrix, an improved breadth-first search algorithm (BFS) is used to calculate candidate outage areas. S6. Apply the preset power outage rule set to the equipment in the candidate power outage area for logical verification to obtain the optimized power outage range.

[0005] S7. Map the optimized power outage area onto the main wiring diagram and use a gradient coloring algorithm to dynamically render the power outage area; for parts that are still energized, overlay flashing warning signs; integrate the list of power outage equipment, operational risk points, and historical fault data to automatically generate detailed maintenance guidance documents.

[0006] Further, step S1 specifically includes: Load the SSD file and main wiring diagram file stored in the substation. Using preset parsing rules and an XML parser, read the contents of the SSD file layer by layer to extract the basic attributes, logical nodes, and connection relationships between IED devices, circuit breakers, disconnect switches, busbars, and load equipment. At the same time, extract the actual installation location and physical connection information of each device from the main wiring diagram to lay the foundation for subsequent data fusion.

[0007] Further, step S2 specifically includes: The device information extracted from the SSD file is compared and matched with the physical wiring data in the main wiring diagram. Based on the device identification, connection port and element information, the logical connection data is corrected to ensure that each device truly reflects its position and connectivity in the substation in the topology model. Then, various devices are abstracted as nodes in the topology model, and weighted edges are constructed for adjacent devices based on the actual electrical connection relationship. The weight of the edge is determined by the device's voltage level, state coefficient and preset weight adjustment coefficient, ultimately generating a dynamically updated power system network model.

[0008] Further, step S3 specifically includes: After selecting the secondary equipment to be inspected, the system automatically calls the SSD file parsing module to extract the primary equipment information corresponding to the secondary equipment; at the same time, it automatically locates and updates its operating status in real time on the main wiring diagram; according to different maintenance tasks, it automatically identifies other equipment connected to the selected equipment that may be affected, and expands these equipment to the queue of equipment to be analyzed to ensure that subsequent calculations cover all potential risk areas.

[0009] Further, step S4 specifically includes: First, the devices in the device queue are marked as visited and added to the backtracking queue. Then, using the reverse link tracing algorithm, the backtracking is performed step by step from each device to be inspected back to the upstream power node. During the backtracking process, the status of each node is checked in turn. If a switch is encountered in a closed state, the tracing continues; if a circuit breaker is encountered in an open state, the tracing is immediately interrupted. All verified valid connected paths are recorded in the electrical connectivity matrix, providing an accurate electrical network basis for subsequent power outage range calculations.

[0010] Further, step S5 specifically includes: Based on the constructed topology model and connectivity verification results, the system uses an improved breadth-first search (BFS) algorithm to calculate the power outage range. First, the equipment to be inspected is added to the BFS queue as the starting point. Then, nodes v in the queue are retrieved one by one, and their voltage levels and operating status are monitored in real time. For nodes in a closed state with voltage levels that reach or exceed a preset voltage threshold, the system marks them as candidate power outage nodes. When a circuit breaker is encountered during the traversal, the expansion of the path is immediately interrupted. All candidate nodes that meet the conditions are clustered according to the connection relationships between devices in the topology model using a connected component algorithm, thereby grouping directly or indirectly connected nodes into a single region.

[0011] Further, step S6 specifically includes: Based on the candidate outage areas, the system further verifies the preset outage rules applied to the equipment within the area to determine whether there are conflicts between the whole-site outage rules and the interval outage rules. The system calculates the load priority of each device based on the device load priority calculation formula, comprehensively considering the load capacity, real-time operating status, and preset priority weights. When a rule conflict occurs, the system selects the device with the highest load priority as the outage boundary node and accordingly merges and optimizes adjacent sub-regions. The method for calculating the load priority is as follows:

[0012] in, Let i be the load capacity of device i. The real-time operating status coefficient of device i is the coefficient during normal operation. When the fault occurs , and This is the priority weighting coefficient.

[0013] Furthermore, the weight of the edge is calculated as follows:

[0014] in, Indicates the voltage level of node i. This represents the equipment state factor when the circuit breaker is closed. When the circuit breaker is tripped ; and Let be the weighting adjustment coefficient, and satisfy . .

[0015] Furthermore, the reverse link tracing algorithm starts from the maintenance equipment node and traces back to the upstream power node level by level, generating a connectivity matrix. Each element The value is 1 if and only if there is at least one connected path between node i and node j, and all switches in the path are in a closed state; specifically, the following steps are included: (1) Initialization: Set up maintenance equipment nodes For nodes that have been visited, add them to the backtracking queue. ; (2) Step-by-step backtracking: Retrieve the current node from the queue. Iterate through all its upstream parent nodes; if the parent node is a power source node, mark the matrix elements. The parent node is added to the queue; if the parent node is a switching device, its status is further determined. If the switch is closed, the backtracking continues; if the switch is open, the backtracking of this path is terminated. (3) Matrix generation: When all backtracking paths have been traversed, the final electrical connectivity verification matrix is ​​obtained.

[0016] This invention also provides an intelligent power outage range calculation system based on SSD files, comprising the following modules: The data acquisition module is used to load the substation SSD file and main wiring diagram file to obtain the logical connection relationship between equipment, actual installation location and physical connection information; The model building module is used to abstract various devices into nodes in the topology model based on the above information, and to build weighted edges for adjacent devices according to the actual electrical connection relationship, thereby generating a dynamically updated power system network model. The equipment identification module is used to automatically identify other equipment connected to the selected secondary equipment that may be affected, and to expand these equipment to the queue of equipment to be analyzed, based on the selected secondary equipment to be inspected. The electrical connectivity verification module is used to verify the electrical connectivity of the above-mentioned equipment queue and obtain the electrical connectivity matrix; The candidate outage area calculation module is used to calculate candidate outage areas based on the power system network model and electrical connectivity matrix, using an improved breadth-first search algorithm (BFS). The power outage boundary optimization module is used to perform logical verification on the equipment within the candidate power outage area using a preset set of power outage rules to obtain the optimized power outage range; The visualization and document generation module maps the optimized power outage area onto the main wiring diagram and uses a gradient coloring algorithm to dynamically render the power outage area; for parts that are still energized, flashing warning signs are overlaid; and it integrates the list of power outage equipment, operational risk points, and historical fault data to automatically generate detailed maintenance guidance documents.

[0017] The advantages of this invention are: This invention automates calculations and analyses by parsing equipment databases, acquiring real-time equipment status information, and combining advanced algorithms. This method effectively improves the accuracy and efficiency of power outage range calculations, significantly reduces the risks of human intervention, and enhances maintenance response speed and operational safety. Through a dynamically generated graphical interface displaying the primary wiring diagram, operators can intuitively understand the scope of power outage impact, achieving visualized and intelligent management of information during equipment maintenance, thereby comprehensively optimizing the operational efficiency and service quality of the power system. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an intelligent power outage range calculation method based on SSD files according to an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the dynamic visualization effect of the power outage area in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 This invention provides an intelligent outage range calculation method based on SSD files, primarily addressing the issue of rapid and accurate delineation of outage areas during substation maintenance, fault location, and safe operation. This method utilizes SSD files and main wiring diagram files to construct a real-time updated power system topology model. Through multi-level data processing, connectivity verification, dynamic range calculation, and multi-rule conflict detection, it ultimately generates an intuitive two-dimensional dynamic display of outage areas. Simultaneously, it automatically generates maintenance guidance documents, providing maintenance personnel with a safe and efficient decision-making basis. Figure 1 As shown, the specific steps include: S1. Load the substation SSD file and main wiring diagram file to obtain the logical connection relationships, actual installation locations, and physical connection information between equipment. The specific implementation method is as follows: Load the SSD file and main wiring diagram file stored in the substation. Using preset parsing rules and an XML parser, read the contents of the SSD file layer by layer to extract the basic attributes, logical nodes, and connection relationships between IED devices, circuit breakers, disconnect switches, busbars, and load equipment. At the same time, extract the actual installation location and physical connection information of each device from the main wiring diagram to lay the foundation for subsequent data fusion.

[0021] S2. Based on the above information, various devices are abstracted as nodes in the topology model, and weighted edges are constructed for adjacent devices according to the actual electrical connection relationships, generating a dynamically updated power system network model. The specific implementation method is as follows: The device information extracted from the SSD file is compared and matched with the physical wiring data in the main wiring diagram. Based on device identifiers, connection ports, and graphic element information, the logical connection data is corrected to ensure that each device accurately reflects its location and connectivity within the substation in the topology model. Then, various devices are abstracted as nodes in the topology model, and weighted edges are constructed for adjacent devices based on their actual electrical connections. The edge weights are determined by the device's voltage level, state coefficient, and a preset weight adjustment coefficient, ultimately generating a dynamically updated power system network model. The edge weights are calculated as follows:

[0022] in, Indicates the voltage level of node i. This represents the equipment state factor when the circuit breaker is closed. When the circuit breaker is tripped ; and Let be the weighting adjustment coefficient, and satisfy . .

[0023] S3. Based on the selected equipment to be inspected, automatically identify other equipment connected to the selected secondary equipment that may be affected, and expand these devices to the queue of equipment to be analyzed. The specific implementation method is as follows: After selecting the secondary equipment to be inspected, the system automatically calls the SSD file parsing module to extract the primary equipment information corresponding to the secondary equipment, such as circuit breakers, disconnect switches, current transformers, and voltage transformers. At the same time, it automatically locates and updates the operating status of the equipment on the main wiring diagram in real time. Based on different inspection tasks (inspection, maintenance, replacement), it automatically identifies other equipment connected to the selected equipment that may be affected, and expands these devices to the queue of equipment to be analyzed, ensuring that subsequent calculations cover all potential risk areas.

[0024] S4. Perform electrical connectivity verification on the above-mentioned equipment queue to obtain the electrical connectivity matrix. The specific implementation method is as follows: First, devices in the device queue are marked as visited and added to the backtracking queue. Then, using a reverse link tracing algorithm, the process starts from each device under maintenance and traces back to the upstream power node. During the backtracking process, the status of each node is checked sequentially. If a switch is encountered in a closed state, the tracing continues; if a circuit breaker is encountered in an open state, the tracing is immediately interrupted. All verified valid connected paths are recorded in the electrical connectivity matrix, providing an accurate electrical network foundation for subsequent power outage range calculations. The reverse link tracing algorithm starts from the device under maintenance node and traces back to the upstream power node, generating a connectivity matrix. Each element The value is 1 if and only if there is at least one connected path between node i and node j, and all switches in the path are in a closed state; specifically, the following steps are included: (1) Initialization: Set up maintenance equipment nodes For nodes that have been visited, add them to the backtracking queue. ; (2) Step-by-step backtracking: Retrieve the current node from the queue. Iterate through all its upstream parent nodes; if the parent node is a power source node, mark the matrix elements. The parent node is added to the queue; if the parent node is a switching device, its status is further determined. If the switch is closed, the backtracking continues; if the switch is open, the backtracking of this path is terminated. (3) Matrix generation: When all backtracking paths have been traversed, the final electrical connectivity verification matrix is ​​obtained.

[0025] S5. Based on the power system network model and electrical connectivity matrix, an improved breadth-first search (BFS) algorithm is used to calculate candidate outage areas. The specific implementation method is as follows: Based on the constructed topology model and connectivity verification results, the system employs an improved Breadth-First Search (BFS) algorithm to calculate the outage area. First, the equipment to be inspected is added to the BFS queue as the starting point. Then, nodes v in the queue are retrieved one by one, and their voltage levels and operating status are monitored in real time. Nodes in a closed state with voltage levels reaching or exceeding a preset voltage threshold are marked as candidate outage nodes. When a circuit breaker is encountered during the traversal, the path extension is immediately interrupted. All candidate nodes meeting the criteria are clustered using a connected component algorithm based on the connection relationships between devices in the topology model, thus grouping directly or indirectly connected nodes into a single region. After the traversal is complete, all candidate nodes meeting the criteria form a preliminary candidate outage area. Next, based on the weighted edge connections between devices in the topology model, the connected component algorithm is applied to cluster these candidate nodes, grouping directly or indirectly connected device nodes into the same sub-region.

[0026] S6. Apply a preset set of power outage rules to the equipment within the candidate power outage area for logical verification to obtain the optimized power outage range. The specific implementation method is as follows: Based on the candidate outage areas, the system further verifies the preset outage rules applied to the equipment within the area to determine whether there are conflicts between the whole-site outage rules and the interval outage rules. The system calculates the load priority of each device based on the device load priority calculation formula, comprehensively considering the load capacity, real-time operating status, and preset priority weights of each device. When rule conflicts occur, the system selects the device with the highest load priority as the outage boundary node and accordingly merges and optimizes the boundaries of adjacent sub-regions. This ensures that the final determined outage range meets safety isolation requirements while minimizing the impact on normal power supply areas. Finally, through multiple processing steps including node extraction, connectivity clustering, and boundary fusion, all device nodes within the candidate outage area that meet the calculation conditions are accurately aggregated to form a dynamically updated final outage range with clear safety isolation boundaries. The load priority calculation method is as follows:

[0027] in, Let i be the load capacity of device i. The real-time operating status coefficient of device i is the coefficient during normal operation. When the fault occurs , and This is the priority weighting coefficient.

[0028] S7. Map the optimized power outage area onto the main wiring diagram and use a gradient coloring algorithm to dynamically render the outage area, intuitively displaying the electrical status of each area; for parts that are still energized, overlay flashing warning signs; integrate the list of outage equipment, operational risk points, and historical fault data, and use natural language processing technology to automatically generate detailed maintenance guidance documents, enabling operators to more intuitively understand and execute power outage operations. Figure 2 As shown in the diagram, the yellow area in the main wiring diagram represents the dynamic rendering effect of the power outage area.

[0029] Example 2 This embodiment also provides an intelligent power outage range calculation system based on SSD files, including the following modules: The data acquisition module is used to load the substation SSD file and main wiring diagram file to obtain the logical connection relationship between equipment, actual installation location and physical connection information; The model building module is used to abstract various devices into nodes in the topology model based on the above information, and to build weighted edges for adjacent devices according to the actual electrical connection relationship, thereby generating a dynamically updated power system network model. The equipment identification module is used to automatically identify other equipment connected to the selected secondary equipment that may be affected, and to expand these equipment to the queue of equipment to be analyzed, based on the selected secondary equipment to be inspected. The electrical connectivity verification module is used to verify the electrical connectivity of the above-mentioned equipment queue and obtain the electrical connectivity matrix; The candidate outage area calculation module is used to calculate candidate outage areas based on the power system network model and electrical connectivity matrix, using an improved breadth-first search algorithm (BFS). The power outage boundary optimization module is used to perform logical verification on the equipment within the candidate power outage area using a preset set of power outage rules to obtain the optimized power outage range; The visualization and document generation module maps the optimized power outage area onto the main wiring diagram and uses a gradient coloring algorithm to dynamically render the power outage area; for parts that are still energized, flashing warning signs are overlaid; and it integrates the list of power outage equipment, operational risk points, and historical fault data to automatically generate detailed maintenance guidance documents.

[0030] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently calculating power outage range based on SSD files, characterized in that, Includes the following steps: S1. Load the substation SSD file and main wiring diagram file to obtain the logical connection relationship between devices, actual installation location and physical connection information; S2. Based on the above information, various devices are abstracted as nodes in the topology model, and weighted edges are constructed for adjacent devices according to the actual electrical connection relationship to generate a dynamically updated power system network model. S3. Based on the selected secondary equipment to be inspected, automatically identify other equipment connected to the selected secondary equipment that may be affected, and extend these equipment to the queue of equipment to be analyzed. S4. Perform electrical connectivity verification on the above equipment queue to obtain the electrical connectivity matrix; S5. Based on the power system network model and electrical connectivity matrix, an improved breadth-first search algorithm (BFS) is used to calculate candidate outage areas. S6. Apply the preset power outage rule set to the equipment in the candidate power outage area for logical verification to obtain the optimized power outage range; S7. Map the optimized power outage area onto the main wiring diagram and use a gradient coloring algorithm to dynamically render the power outage area; for parts that are still energized, overlay flashing warning signs; integrate the list of power outage equipment, operational risk points, and historical fault data to automatically generate detailed maintenance guidance documents.

2. The intelligent power outage range calculation method based on SSD files according to claim 1, characterized in that, Step S1 specifically involves: Load the SSD file and main wiring diagram file stored in the substation; using preset parsing rules and an XML parser, read the contents of the SSD file layer by layer to extract the basic attributes, logical nodes, and connection relationships between IED devices, circuit breakers, disconnect switches, busbars, and load equipment; at the same time, extract the actual installation location and physical connection information of each device from the main wiring diagram to lay the foundation for subsequent data fusion.

3. The intelligent power outage range calculation method based on SSD files according to claim 1, characterized in that, Step S2 specifically involves: The device information extracted from the SSD file is compared and matched with the physical wiring data in the main wiring diagram. Based on the device identification, connection port and element information, the logical connection data is corrected to ensure that each device truly reflects its position and connectivity in the substation in the topology model. Then, various devices are abstracted as nodes in the topology model, and weighted edges are constructed for adjacent devices based on the actual electrical connection relationship. The weight of the edge is determined by the device's voltage level, state coefficient and preset weight adjustment coefficient, ultimately generating a dynamically updated power system network model.

4. The intelligent power outage range calculation method based on SSD files according to claim 1, characterized in that, Step S3 specifically involves: After selecting the secondary equipment to be inspected, the system automatically calls the SSD file parsing module to extract the primary equipment information corresponding to the secondary equipment; at the same time, it automatically locates and updates its operating status in real time on the main wiring diagram; according to different maintenance tasks, it automatically identifies other equipment connected to the selected equipment that may be affected, and expands these equipment to the queue of equipment to be analyzed to ensure that subsequent calculations cover all potential risk areas.

5. The intelligent power outage range calculation method based on SSD files according to claim 1, characterized in that, Step S4 specifically involves: First, mark the devices in the device queue as visited and add them to the backtracking queue; then, using the reverse link tracing algorithm, start from each device to be inspected and backtrack level by level to the upstream power node; during the backtracking process, check the status of each node in turn; If a switch is encountered in a closed state, the tracing continues; if a circuit breaker is encountered in an open state, the tracing is immediately interrupted. All verified valid connection paths are recorded in the electrical connectivity matrix, providing an accurate electrical network basis for subsequent power outage range calculations.

6. The intelligent power outage range calculation method based on SSD files according to claim 1, characterized in that, Step S5 specifically involves: Based on the constructed topology model and connectivity verification results, the system uses an improved breadth-first search (BFS) algorithm to calculate the power outage range. First, the equipment to be inspected is added to the BFS queue as the starting point. Then, nodes v in the queue are retrieved one by one, and their voltage levels and operating status are monitored in real time. For nodes in a closed state with voltage levels that reach or exceed a preset voltage threshold, the system marks them as candidate power outage nodes. When a circuit breaker is encountered during the traversal, the expansion of the path is immediately interrupted. All candidate nodes that meet the conditions are clustered according to the connection relationships between devices in the topology model using a connected component algorithm, thereby grouping directly or indirectly connected nodes into a single region.

7. The intelligent power outage range calculation method based on SSD files according to claim 1, characterized in that, Step S6 specifically involves: Based on the candidate outage areas, the system further verifies the preset outage rules applied to the equipment within the area to determine whether there is a conflict between the whole-station outage rules and the interval outage rules. The system calculates the load priority of each equipment by comprehensively considering the load capacity, real-time operating status and preset priority weight of each equipment according to the equipment load priority calculation formula. When rule conflicts occur, the system selects the device with the highest load priority as the power outage boundary node, and accordingly merges and optimizes adjacent sub-regions; the load priority is calculated as follows: in, Let i be the load capacity of device i. This is the real-time operating status coefficient of device i, during normal operation. When the fault occurs , and This is the priority weighting coefficient.

8. The intelligent power outage range calculation method based on SSD files according to claim 3, characterized in that, The weight of the edge is calculated as follows: in, Indicates the voltage level of node i. This represents the equipment state factor when the circuit breaker is closed. When the circuit breaker is tripped ; and Let be the weighting adjustment coefficient, and satisfy . .

9. The intelligent power outage range calculation method based on SSD files according to claim 5, characterized in that, The reverse link tracing algorithm starts from the maintenance equipment node and traces back to the upstream power supply node level by level, generating a connectivity matrix. Each element The value is 1 if and only if there is at least one connected path between node i and node j, and all switches in the path are in a closed state; specifically, the following steps are included: (1) Initialization: Set up maintenance equipment nodes For nodes that have been visited, add them to the backtracking queue. ; (2) Step-by-step backtracking: Retrieve the current node from the queue. Iterate through all its upstream parent nodes; if the parent node is a power source node, mark the matrix elements. The parent node is added to the queue; if the parent node is a switching device, its status is further determined. If the switch is closed, the backtracking continues; if the switch is open, the backtracking of this path is terminated. (3) Matrix generation: When all backtracking paths have been traversed, the final electrical connectivity verification matrix is ​​obtained.

10. A smart power outage range calculation system based on SSD files, characterized in that, Includes the following modules: The data acquisition module is used to load the substation SSD file and main wiring diagram file to obtain the logical connection relationship between equipment, actual installation location and physical connection information; The model building module is used to abstract various devices into nodes in the topology model based on the above information, and to build weighted edges for adjacent devices according to the actual electrical connection relationship, thereby generating a dynamically updated power system network model. The equipment identification module is used to automatically identify other equipment connected to the selected secondary equipment that may be affected, and to expand these equipment to the queue of equipment to be analyzed, based on the selected secondary equipment to be inspected. The electrical connectivity verification module is used to verify the electrical connectivity of the above-mentioned equipment queue and obtain the electrical connectivity matrix; The candidate outage area calculation module is used to calculate candidate outage areas based on the power system network model and electrical connectivity matrix, using an improved breadth-first search algorithm (BFS). The power outage boundary optimization module is used to perform logical verification on the equipment within the candidate power outage area using a preset set of power outage rules to obtain the optimized power outage range; The visualization and document generation module maps the optimized power outage area onto the main wiring diagram and uses a gradient coloring algorithm to dynamically render the power outage area; for parts that are still energized, flashing warning signs are overlaid; and it integrates the list of power outage equipment, operational risk points, and historical fault data to automatically generate detailed maintenance guidance documents.

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