Fault isolation method, system and device of power Internet of Things device and medium

Abstract

The invention discloses a fault isolation method, system and device for power Internet of Things equipment and a medium, and the method comprises the steps: obtaining an electrical signal of each piece of power equipment in the power Internet of Things, and determining a fault device and a fault position based on the electrical signal; based on the power topological structure diagram, determining the number of first communication paths and the number of second communication paths corresponding to the fault equipment and the fault communication nodes, and based on the number of the first communication paths and the number of the second communication paths, determining an isolation boundary node set for isolating the fault equipment, calculating and simulating the change quantity of a communication path of each node in the power Internet of Things after the isolation boundary node set is disconnected; and adjusting the isolation boundary node set based on the comparison result of the variable quantity of each communication path and the preset threshold value so as to obtain the target isolation range meeting the preset condition, and isolating the power equipment in the target isolation range, so that the accuracy of isolation range delimitation after the fault occurs can be improved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a fault isolation method for power Internet of Things (IoT) devices. Background Technology

[0002] When a power system fault occurs, defining the isolation zone is a crucial measure for rapid fault handling and preventing the spread of risk. If isolation boundaries are not defined promptly and accurately, abnormal electrical conditions such as overcurrent and voltage drops caused by the fault can propagate rapidly along the continuity path, potentially damaging adjacent healthy equipment, triggering cascading faults, or even escalating into a regional blackout. Therefore, defining the isolation zone can quickly disconnect faulty equipment or sections from the normal operating network, thereby blocking the fault current path, stabilizing system voltage, and creating safe conditions for subsequent restoration operations.

[0003] Existing technologies typically rely on isolation strategies based on fixed rules or on dispatchers' experience to manually define boundaries. These methods have significant shortcomings in accuracy: First, they ignore the dynamic and varied number of connection paths in the power grid topology, potentially leading to insufficient isolation in densely populated node areas and excessive isolation in sparsely populated critical hubs. Second, they cannot quantify the damage isolation actions cause to the system's self-healing capabilities, often blindly expanding the scope in pursuit of thorough isolation, cutting off backup connection paths that could serve as recovery channels. This results in the system losing its rapid power transfer capability despite the fault being isolated, and the recovery time actually being prolonged. Summary of the Invention

[0004] This invention provides a fault isolation method, system, device, and medium for power Internet of Things (IoT) devices, which can improve the accuracy of the isolation range delineation after a fault occurs, thereby improving the fault self-healing capability.

[0005] An embodiment of the present invention provides fault isolation for power Internet of Things (IoT) devices, including: Acquire electrical signals from each power device in the power Internet of Things, and determine the faulty device and fault location based on each electrical signal; Based on the power topology diagram, the number of first and second connected paths corresponding to the faulty equipment and each faulty connected node are determined respectively. Based on the number of first connected paths and each of the second connected paths, an isolation boundary node set for isolating the faulty equipment is determined, and the change in the connected paths of each node in the power Internet of Things after simulating disconnection of the isolation boundary node set is calculated. Each faulty connected node is determined based on the fault location. Based on the comparison results of the changes in each of the connected paths with the preset threshold, the isolation boundary node set is adjusted to obtain the target isolation range that meets the preset conditions, and the power equipment within the target isolation range is isolated.

[0006] This invention, based on electrical signals, enables fault diagnosis, allowing for earlier and more accurate location of the specific device experiencing an electrical fault. This provides an accurate and reliable fault origin for subsequent isolation operations. By determining the number of first and second connected paths, the connection density and importance of the faulty device node and faulty connected nodes in the network can be obtained, providing a structured data foundation for assessing the potential impact of the fault and selecting key isolation points. Based on the number of connected paths, a set of isolation boundary nodes capable of effectively separating the faulty device from the network is initially selected. By simulating the change in the connected paths of each node in the network after disconnecting the isolation boundary node set, the invention achieves [further analysis / control]. Quantitative prediction and comparison of the network impact caused by different isolation schemes transforms isolation decisions from empirical judgments to an optimization process based on network performance change data. This allows for the pre-assessment of the impact of each isolation scheme on the power supply reliability and network structural integrity of non-faulty areas. By comparing the simulated "change in connectivity paths" with a "preset threshold," the reasonableness of the initially defined isolation range can be automatically determined. If the impact is too large, the isolation boundary node set is adjusted accordingly; if the impact is within acceptable limits, the final scheme is confirmed. This adjustment mechanism ensures that the final determined "target isolation range" achieves an optimal balance between reliably isolating faults and maximizing power supply to non-faulty areas. Isolation based on this target isolation range effectively prevents fault propagation and creates clear and optimized network boundary conditions for rapid power restoration. Compared with existing technologies, this invention improves the accuracy of isolation range delineation after a fault occurs, thereby enhancing fault self-healing capabilities.

[0007] Furthermore, determining the faulty device and fault location based on each of the electrical signals includes: The rate of change of the signal at each node between adjacent sampling points is calculated based on the electrical signal. When the rate of change of the signal exceeds a preset rate of change threshold, an abnormal band signal is identified, and the signal amplitude deviation and phase offset in the abnormal band signal are extracted as fault features. The fault characteristics are matched with the device characteristics in the power topology diagram to determine the faulty device and the location of the fault.

[0008] This fault diagnosis based on electrical signals allows for earlier and more accurate location of the specific equipment experiencing the electrical fault, thus providing an accurate and reliable fault origin for subsequent isolation operations.

[0009] Further, determining the number of first and second connected paths corresponding to the faulty device and each faulty connected node based on the power topology diagram includes: Based on the fault location, a fault area is delineated in the power topology diagram, and several fault connection nodes with power connection to the faulted equipment are identified in the fault area. The number of first connected paths originating from the faulty device is calculated using graph theory algorithms, and the number of second connected paths originating from each of the faulty connected nodes is calculated.

[0010] By determining the number of first and second connected paths, we can obtain the connection density and importance of faulty device nodes and faulty connected nodes in the network, providing a structured data foundation for assessing the potential impact of the fault and selecting key isolation points.

[0011] Further, determining the isolation boundary node set for isolating the faulty device based on the number of the first connected paths and the number of each of the second connected paths includes: Calculate the ratio of the number of second connected paths to the number of first connected paths to obtain the path connectivity ratio of each node; If the connectivity ratio of each path is greater than a preset threshold, the corresponding faulty connectivity nodes are aggregated to determine the isolation boundary node set used to isolate the faulty device.

[0012] Based on the number of connected paths, a preliminary set of isolated boundary nodes that can effectively separate faulty devices from the network is selected.

[0013] Furthermore, the calculation of the change in connectivity paths of each node in the power Internet of Things after disconnecting the isolated boundary node set includes: Based on the isolated boundary node set, the disconnection of the isolated boundary node set is simulated in the network connectivity model to obtain simulation results, wherein the network connectivity model is constructed based on the power topology diagram; Based on the simulation results, the number of third connected paths for each node is calculated; Based on the number of the third and fourth connected paths, the change in connected paths for each node is calculated, wherein the number of the fourth connected paths is calculated for each node in the network connectivity model.

[0014] By simulating and calculating the changes in the connectivity paths of each node in the network after disconnecting the isolation boundary node set, the network impact caused by different isolation schemes can be quantitatively predicted and compared. This transforms isolation decision-making from empirical judgment to an optimization process based on network performance change data, and enables the pre-assessment of the impact of each isolation scheme on the power supply reliability and network structure integrity of non-faulty areas.

[0015] Further, the adjustment of the isolation boundary node set based on the comparison results of the changes in each of the connected paths and the preset threshold, to obtain a target isolation range that meets the preset conditions, includes: When the change in the connected path is greater than the preset threshold, the corresponding set of isolated boundary nodes is taken as the risk boundary node, and the risk boundary node is removed from the set of isolated boundary nodes to obtain the first processing result. When the changes in the connectivity path are all less than or equal to the preset threshold and the faulty device is not completely isolated, at least one adjacent node that is closest to the faulty device in electrical distance is added to update the isolation boundary node set, and a second processing result is obtained. The target isolation range is determined based on the first processing result and the second processing result until the first processing result and the second processing result meet the preset conditions.

[0016] By comparing the simulated "change in connectivity path" with the "preset threshold", the system can automatically determine whether the initially defined isolation range is reasonable. If the impact is too great, the isolation boundary node set will be adjusted accordingly. If the impact is within an acceptable range, the final solution will be confirmed. This adjustment mechanism ensures that the final "target isolation range" achieves the optimal balance between reliably isolating faults and maximizing power supply to non-faulty areas.

[0017] Furthermore, isolating the power equipment within the target isolation range includes: sending an isolation command containing a device identifier and a disconnection operation to all power equipment within the target isolation range, so as to control the target power equipment corresponding to the device identifier to perform a disconnection operation.

[0018] Another embodiment of the present invention provides a fault isolation system for power Internet of Things (IoT) devices, comprising: The acquisition module is used to acquire electrical signals of each power device in the power Internet of Things, and determine the faulty device and the fault location based on each electrical signal; The processing module is used to determine the number of first and second connected paths corresponding to the faulty device and each faulty connected node based on the power topology diagram; to determine the isolation boundary node set for isolating the faulty device based on the number of first connected paths and each of the second connected paths; and to calculate the change in the connected paths of each node in the power Internet of Things after simulating the disconnection of the isolation boundary node set, wherein each faulty connected node is determined based on the fault location. An isolation module is used to adjust the isolation boundary node set based on the comparison results of the change amount of each connected path with a preset threshold, so as to obtain a target isolation range that meets the preset conditions, and to isolate the power equipment within the target isolation range.

[0019] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the fault isolation method for power Internet of Things devices as described in the present invention.

[0020] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the fault isolation method for power Internet of Things devices as described in the present invention. Attached Figure Description

[0021] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an embodiment of the fault isolation method for power Internet of Things (IoT) devices provided in this application; Figure 2 This is a flowchart illustrating one embodiment of steps S201 to S202 provided in this application; Figure 3 This is a flowchart illustrating one embodiment of steps S301 to S303 provided in this application; Figure 4 This is a flowchart illustrating one embodiment of steps S401 to S402 provided in this application; Figure 5 This is a schematic diagram of an embodiment of the fault isolation method for power Internet of Things (IoT) devices provided in this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0029] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0030] When a power system fault occurs, timely and accurate delineation of isolation boundaries is a key measure to achieve rapid fault handling and prevent the spread of risks. Existing technologies typically rely on isolation strategies based on fixed rules or on dispatchers' experience to manually delineate boundaries, which has significant shortcomings in accuracy.

[0031] See Figure 1 To improve the accuracy of isolation range delineation after a fault occurs, and thus enhance the fault self-healing capability, an embodiment of the present invention provides a fault isolation method for power Internet of Things (IoT) devices, comprising steps S101 to S103: Step S101: Obtain the electrical signals of each power device in the power Internet of Things, and determine the faulty device and fault location based on each electrical signal; In some embodiments, acquiring electrical signals from various power devices in the power Internet of Things (IoT) involves the following steps: First, based on the physical topology and communication architecture of the power system, intelligent monitoring terminals and corresponding sensors are deployed at key power device nodes. These terminals and sensors constitute the IoT sensing layer, responsible for real-time acquisition of raw electrical signals, including but not limited to three-phase voltage, three-phase current, and power. Then, the acquired raw electrical signals are uploaded to edge computing nodes or the main station system via the power IoT communication network. After uploading, the raw electrical signals undergo preprocessing to ensure data quality, thereby obtaining the final electrical signals.

[0032] It should be noted that power equipment nodes can include, but are not limited to, substation outgoing switches, sectionalizing switches, tie switches, distribution transformers, distributed power grid connection points, and important load access points.

[0033] It should be noted that the monitoring terminals mainly adopt intelligent terminals such as feeder terminal units (FTU), distribution terminal units (DTU), and transformer terminal units (TTU), and are equipped with sensors such as current transformers (CT) and voltage transformers (PT).

[0034] It should be noted that the communication network can be a dedicated fiber optic network, a dedicated wireless network (such as LTE / 5G), or a broadband carrier network following standards such as High-Speed ​​Power Line Carrier (HPLC), depending on the site conditions. Data transmission follows unified protocols (such as IEC 61850 and IEC 104 protocols) and message formats.

[0035] It should be noted that the preprocessing steps include: First, performing CRC verification and integrity checks on the received data packets to remove invalid, distorted, or timed-out data packets; then, using the Precision Clock Protocol (PTP) based on IEEE 1588 or a satellite synchronization clock to perform high-precision synchronization of the sampling time of all monitoring nodes to ensure that the data of the entire network has a unified time reference; then, using a digital filter to filter the analog signal to extract clean power frequency signal components.

[0036] In some embodiments, determining the faulty device and fault location based on the electrical signals includes: calculating the signal change rate of each node between adjacent sampling points based on the electrical signals; when the signal change rate exceeds a preset change rate threshold, identifying an abnormal band signal and extracting the signal amplitude deviation and phase offset from the abnormal band signal as fault features; matching the fault features with device features in the power topology diagram to determine the faulty device and fault location. Specifically, firstly, a differential comparison algorithm is used to calculate the instantaneous change rate (e.g., ΔI / Δt, ΔU / Δt) of the electrical signals (especially current and voltage) of each node between adjacent high-frequency sampling points in real time; then, the calculated signal change rate is compared with a preset change rate threshold; when the signal change rate of a node exceeds its threshold for multiple consecutive sampling periods, it is determined that the node has an abnormal band signal, and the start time of the abnormality is recorded. Next, within the locked time window of the abnormal band signal, multi-dimensional fault feature extraction is performed: on the one hand, the signal amplitude deviation is obtained by calculating the percentage difference between the effective value of the fundamental wave and the steady-state reference value before the fault during the abnormal period; on the other hand, the spectrum of the abnormal band is analyzed using Fast Fourier Transform (FFT) to extract the phase information of the fundamental wave component, and the phase shift is calculated by comparing it with the normal phase angle. These amplitude deviations, phase shifts, and the duration of the abnormality together constitute the fault features. Finally, the system compares the calculated fault characteristics with the device characteristics at all possible locations in a pre-stored device characteristic library built based on the power topology diagram. Matching calculations are performed by traversing and searching the fault feature mapping database, using a similarity calculation algorithm (e.g., calculating...). With each of the libraries (Weighted Euclidean distance or cosine similarity) to find the similarity with The one or more candidate theoretical vectors with the highest similarity will ultimately be used to determine the most likely faulty device and fault location for this fault, based on the device identifier and fault location associated with the best matching theoretical vector.

[0037] It should be noted that the rate of change threshold is set comprehensively based on the equipment's rated parameters, historical normal operation data, and system operation mode (for example, the voltage rate of change threshold is 15% of the rated value per sampling period, and the current threshold is 20% per sampling period). The specific setting method is not the focus of this application, so it will not be elaborated here.

[0038] It should be noted that during the offline phase, for each monitorable power device in the diagram (such as line segment L12 and transformer T1), typical fault types that may occur are preset (for example, for line L12, fault types are set to include single-phase grounding, two-phase short circuit, two-phase ground short circuit, and three-phase short circuit). Subsequently, in the simulation model, various faults are simulated sequentially on the device, and through simulation calculations, the theoretical changes in electrical signals at adjacent key monitoring nodes upstream and downstream when the fault occurs are obtained. For each monitoring point, its theoretical fault feature vector is extracted. An exemplary vector can be represented as... Where ΔI% represents the percentage change in current amplitude at the monitoring point relative to its rated value; ΔV% represents the percentage change in voltage amplitude at the monitoring point relative to its rated value; Δθ represents the phase angle offset between voltage and current at the monitoring point; and t represents the characteristic duration of the fault transient process. Finally, the theoretical fault feature vector generated by each device at different monitoring points under different fault types is calculated. This is associated with the device identifier and its fault location information to form a structured fault feature mapping library.

[0039] It should be noted that the matching algorithm can use methods such as calculating similarity (e.g., cosine similarity) or distance (e.g., weighted Euclidean distance) to evaluate the matching probability between real-time features and each theoretical model.

[0040] This fault diagnosis based on electrical signals allows for earlier and more accurate location of the specific equipment experiencing the electrical fault, thus providing an accurate and reliable fault origin for subsequent isolation operations.

[0041] Step S102: Based on the power topology diagram, determine the number of first and second connected paths corresponding to the faulty device and each faulty connected node. Based on the number of first and second connected paths, determine the isolation boundary node set used to isolate the faulty device, and calculate the change in the connected paths of each node in the power Internet of Things after simulating disconnection of the isolation boundary node set. Each faulty connected node is determined based on the fault location. Please refer to Figure 2 In some embodiments, determining the number of first and second connected paths corresponding to the faulty device and each faulty connected node based on the power topology diagram includes steps S201 to S202: Step S201: Based on the fault location, delineate the fault area in the power topology diagram, and determine a number of fault connection nodes in the fault area that have a power connection relationship with the faulty equipment; In some embodiments, firstly, the fault area is defined centered on the fault location based on the number of direct connections between devices via lines, i.e., the number of topology hops. The fault area is typically defined as including the faulty device itself and all nodes within its N-hop range (e.g., N=2 or 3, configurable according to network density). Then, within the defined fault area, all nodes are traversed, and by querying the adjacency matrix or adjacency table of the topology graph, all nodes with a direct electrical connection to the faulty device (i.e., directly connected via a line or switch, with no other devices in between) are selected and defined as fault-connected nodes.

[0042] It should be noted that the fault-connected node is the first neighboring node affected by the fault current or voltage disturbance, and it is also the key object for subsequent analysis of the fault propagation path and determination of the isolation boundary.

[0043] Step S202: Calculate the number of first connected paths originating from the faulty device using a graph theory algorithm, and calculate the number of second connected paths originating from each of the faulty connected nodes.

[0044] In some embodiments, firstly, the power topology is abstracted as an undirected graph G=(V, E), where the vertex set V represents various power equipment nodes (switches, transformer terminals, bus connection points, etc.), and the edge set E represents the electrical connection lines between equipment. The set of all power source points (such as substation buses, distributed power grid connection points) in the power topology graph is defined as S⊆V, where a connected path is a simple path from a starting node to any power source point S without repeating nodes or edges. Then, after obtaining the faulty connected nodes, starting from the faulty equipment node, an improved depth-first search (DFS) or breadth-first search (BFS) algorithm is used to traverse the undirected graph to find all connected paths leading to the set of power source points S, and redundant paths sharing intermediate nodes or lines are eliminated. Finally, the total number of independent, disjoint paths from the faulty equipment to all power source points is counted, i.e., the number of first connected paths. Similarly, for each faulty connected node, starting from the faulty connected node, repeat the same path search process to calculate the total number of independent paths from it to the set of all power points S, and obtain the number of second connected paths for that node.

[0045] It's important to note that Depth-First Search (DFS) is a graph traversal algorithm that starts from the starting point and explores a path in depth to the end before backtracking. It is implemented using a system stack or recursion and is commonly used for path discovery and topology sorting. Breadth-First Search (BFS) is also a graph traversal algorithm that starts from the starting point and traverses all its neighboring nodes layer by layer. It is implemented using a queue and is effective for finding the shortest path or analyzing network layers.

[0046] It should be noted that the number of the first connected paths reflects the connectivity strength and power supply reliability margin of the faulty device itself in the network. The number of the second connected paths reflects the connectivity strength of each of its direct neighbor nodes.

[0047] By determining the number of first and second connected paths, we can obtain the connection density and importance of faulty device nodes and faulty connected nodes in the network, providing a structured data foundation for assessing the potential impact of the fault and selecting key isolation points.

[0048] In some embodiments, determining the isolation boundary node set for isolating the faulty device based on the number of the first connected paths and the number of each of the second connected paths includes: calculating the ratio of the number of each of the second connected paths to the number of the first connected paths to obtain the path connectivity ratio of each node; If the connectivity ratio of each path is greater than a preset threshold, then the corresponding faulty connected nodes are aggregated to determine the isolation boundary node set used to isolate the faulty device. Specifically, firstly, when the number of first connected paths is obtained... and the number of each of the second connected paths Then, calculate the path connectivity ratio for each node one by one. The calculation formula is: , where the ratio This characterizes the relative strength of the connectivity of the faulty node compared to the faulty device itself. Then, the comparison logic for each calculated path is as follows: If... If the connectivity of faulty node i is significantly stronger than that of the faulty device, then selecting faulty node i as the isolation point can effectively cut off the connection between the faulty device and the outside world, while also utilizing its strong multipath connectivity to provide more power restoration or load transfer channels for the non-faulty areas outside the isolation zone. Therefore, all nodes that meet the requirements... The collection of faulty connected nodes can form the initial isolation boundary node set. .

[0049] It should be noted that the ratio threshold α is not a fixed value, but is dynamically set according to the average redundancy of the power grid topology and the regional power supply reliability requirements. For example, it can be set to 1.2 in dense ring network areas and 0.8 in radial networks.

[0050] It should be noted that the number of connected paths is the total number of simple paths in the power grid topology graph G=(V,E) that start from a node and reach any node in any specified set of power nodes S, and that do not repeatedly pass through any vertex except for the starting point and the ending point.

[0051] It should be noted that the system will perform data validation during the calculation process, for example, if If the value is zero (which should not theoretically occur in a connected network), the abnormal state will be reported and the preset default handling strategy will be enabled.

[0052] Based on the number of connected paths, a preliminary set of isolated boundary nodes that can effectively separate faulty devices from the network is selected.

[0053] Please refer to Figure 3 In some embodiments, the calculation of the change in connectivity paths of each node in the power Internet of Things after disconnecting the isolation boundary node set includes steps S301 to S303: Step S301: Based on the isolated boundary node set, simulate the disconnection of the isolated boundary node set in the network connectivity model to obtain simulation results, wherein the network connectivity model is constructed based on the power topology diagram; In some embodiments, firstly, based on the undirected graph G=(V, E) constructed from the power topology graph, the state attributes of each node are maintained / attached, and graph theory algorithms for calculating connected paths are integrated to obtain a network connectivity model, which is typically implemented using an adjacency matrix or adjacency list data structure. Simultaneously, before a fault occurs or at the start of the analysis, the number of connected paths for each node v in the network connectivity model under the original normal state is calculated and stored, denoted as the fourth connected path number. Then, in the network connectivity model in memory, the initial set of isolated boundary nodes is... All nodes are marked as logically disconnected, meaning that the row and column elements associated with these nodes are set to zero in the adjacency matrix, or these nodes and all their associated edges are removed from the adjacency list, to obtain the simulated network topology, i.e., the simulation result. .

[0054] It should be noted that the simulation is performed only in memory and does not affect the actual physical device's operating status. The simulation results include the updated network topology and the reachability markers of each node in the disconnected state.

[0055] Step S302: Based on the simulation results, calculate the number of third connected paths for each node; In some embodiments, when simulation results are obtained Subsequently, since the critical boundary nodes have been logically removed, the network topology has changed. Therefore, the set of reachable paths for each node (especially those located downstream of the fault region or closely associated with the disconnected node) may decrease. Thus, it is necessary to re-search for all independent electrical paths to all preset power points, starting from each node v in the current network model. This involves using a similar calculation method to determine the number of third connected paths for each node in this simulated isolation scenario. .

[0056] Step S303: Based on the number of third connected paths and the number of fourth connected paths, calculate the change in connected paths for each node, wherein the number of fourth connected paths is calculated for each node in the network connectivity model.

[0057] In some embodiments, when the number of third connected paths for each node after simulated isolation is obtained... and the number of fourth connected paths Then, a subtraction operation is performed on each node v, which is to calculate the change in the connected path. The calculation formula is: It should be noted that the change in the connected path It is a signed integer, for most non-faulty region nodes. This indicates that the node was not affected. This applies to nodes that lost part of their power supply path due to the isolation scheme (usually downstream of the fault area or nodes closely associated with the disconnected node). ,in, The larger the absolute value, the greater the loss of power supply reliability margin due to this isolation, and the more severe the impact.

[0058] It should be noted that the calculation methods for the number of third and fourth connected paths are similar to those in step S202 above, which have been explained in detail above, so they will not be repeated here.

[0059] By simulating and calculating the changes in the connectivity paths of each node in the network after disconnecting the isolation boundary node set, the network impact caused by different isolation schemes can be quantitatively predicted and compared. This transforms isolation decision-making from empirical judgment to an optimization process based on network performance change data, and enables the pre-assessment of the impact of each isolation scheme on the power supply reliability and network structure integrity of non-faulty areas.

[0060] Step S103: Based on the comparison results of the change amount of each connected path with the preset threshold, the isolation boundary node set is adjusted to obtain the target isolation range that meets the preset conditions, and the power equipment within the target isolation range is isolated.

[0061] Please refer to Figure 4 In some embodiments, adjusting the isolation boundary node set based on the comparison results of the changes in each of the connected paths with a preset threshold to obtain a target isolation range that meets preset conditions includes steps S401 to S403: Step S401: When the change in the connected path is greater than the preset threshold, the corresponding set of isolated boundary nodes is taken as the risk boundary node, and the risk boundary node is removed from the set of isolated boundary nodes to obtain the first processing result. In some embodiments, the change in connectivity path Each node is compared one by one with a preset threshold β. When the system determines the boundary node... When the value is greater than β, it indicates that disconnecting this node will result in excessive loss of connectivity paths in the non-faulty area, severely impairing its self-healing ability. Therefore, it is marked as a risk boundary node. Subsequently, the risk boundary node is removed from the current set of isolated boundary nodes to be executed, generating a new boundary set that excludes all high-impact nodes, thus obtaining the first processing result.

[0062] It should be noted that the preset threshold β is set according to the network size and reliability level (for example, it can be set to 3 for a medium-sized distribution network, meaning that the path loss of a single node does not exceed 3).

[0063] Step S402: When the changes in the connected paths are all less than or equal to the preset threshold and the faulty device is not completely isolated, add at least one adjacent node that is closest to the faulty device in electrical distance to update the isolation boundary node set and obtain the second processing result. In some embodiments, when verifying the change in connectivity paths of all currently isolated boundary nodes... All meet When ≤ β, the effectiveness of the isolation is further evaluated. At this point, using the faulty device as the source, a breadth-first search (BFS) is performed in the graph theory model to check if there exists any electrical path to the non-faulty region that is not completely blocked by the current set of boundary nodes. If such a path exists, the faulty device is determined not to be completely isolated. Then, the electrical distances from the faulty device to all its directly electrically connected nodes are calculated, and at least one adjacent node with the closest electrical distance is selected and added to the current isolation boundary node set, thereby generating an updated, expanded boundary set, which is the second processing result.

[0064] It should be noted that when multiple electrical distances are the same, the adjacent node with the larger number of second connected paths is selected to enhance boundary redundancy.

[0065] Step S403: Until the first processing result and the second processing result meet the preset conditions, determine the target isolation range based on the first processing result and the second processing result.

[0066] In some embodiments, after obtaining the first and second processing results in each calculation, it is necessary to re-simulate and calculate the change in connectivity paths and perform another effectiveness evaluation until the faulty device is completely isolated (no non-faulty area nodes can be accessed from the faulty device) and the change in connectivity paths of all non-faulty nodes is calculated. Once all values ​​do not exceed the preset threshold β, the iteration will terminate, and the final boundary nodes will be grouped into a closed area enclosed on the power topology diagram, which will be determined as the target isolation range. At this point, all devices (including faulty devices) within this range are the objects that need to be isolated.

[0067] By comparing the simulated "change in connectivity path" with the "preset threshold", the system can automatically determine whether the initially defined isolation range is reasonable. If the impact is too great, the isolation boundary node set will be adjusted accordingly. If the impact is within an acceptable range, the final solution will be confirmed. This adjustment mechanism ensures that the final "target isolation range" achieves the optimal balance between reliably isolating faults and maximizing power supply to non-faulty areas.

[0068] In some embodiments, isolating the power devices within the target isolation range includes: sending an isolation command containing a device identifier and a disconnection operation to all power devices within the target isolation range, so as to control the target power device corresponding to the device identifier to perform a disconnection operation.

[0069] In some embodiments, firstly, based on a pre-established device mapping database, the determined target isolation range is converted into a list of executable operation instructions. For each target power device in the operation instruction list, a structured isolation instruction is generated. This isolation instruction contains at least two core fields: a device identifier for precise addressing of the target device in the network, and a disconnection operation, explicitly specifying the requirement for the device to perform a "shutdown" action. These instructions are transmitted to edge intelligent terminals or intelligent operating mechanisms deployed at the equipment site via reliable communication channels of the power Internet of Things (such as fiber optic private networks, 5G slicing, or secure encrypted wireless networks) using standard protocols (such as IEC 60870-5-104, DNP3, or MQTT). Then, upon receiving the instruction, the terminal device first performs security verification and authorization verification. After confirming that everything is correct, it then drives its actuator (such as a tripping coil) to control the corresponding circuit breaker or load switch to complete the physical disconnection.

[0070] It should be noted that the conversion process maps logical "nodes" or "boundary locations" to specific physical power equipment identifiers with remote control capabilities, such as the unique code of a smart circuit breaker on a specific feeder (e.g., LD / LN name conforming to IEC61850 or device ID based on dispatch number).

[0071] It should be noted that, to ensure the security and reliability of the operation, the isolation command may also include an operation sequence number (for tracking and confirmation), a planned execution timestamp (supporting delayed or synchronous execution), and a security check code (such as a digital signature, used to prevent accidental operation or malicious commands).

[0072] It should be noted that after the operation is completed, the intelligent terminal will send the new status of the equipment (such as "shutdown in place") and the corresponding remote signaling signal to the master station system in real time. The master station system compares the instruction list with the feedback status to confirm that all designated devices within the target isolation range have been successfully disconnected, thus achieving complete fault isolation both logically and physically, forming an operation closed loop.

[0073] This invention, based on electrical signals, enables fault diagnosis, allowing for earlier and more accurate location of the specific device experiencing an electrical fault. This provides an accurate and reliable fault origin for subsequent isolation operations. By determining the number of first and second connected paths, the connection density and importance of the faulty device node and faulty connected nodes in the network can be obtained, providing a structured data foundation for assessing the potential impact of the fault and selecting key isolation points. Based on the number of connected paths, a set of isolation boundary nodes capable of effectively separating the faulty device from the network is initially selected. By simulating the change in the connected paths of each node in the network after disconnecting the isolation boundary node set, the invention achieves [further analysis / control]. Quantitative prediction and comparison of the network impact caused by different isolation schemes transforms isolation decisions from empirical judgments to an optimization process based on network performance change data. This allows for the pre-assessment of the impact of each isolation scheme on the power supply reliability and network structural integrity of non-faulty areas. By comparing the simulated "change in connectivity paths" with a "preset threshold," the reasonableness of the initially defined isolation range can be automatically determined. If the impact is too large, the isolation boundary node set is adjusted accordingly; if the impact is within acceptable limits, the final scheme is confirmed. This adjustment mechanism ensures that the final determined "target isolation range" achieves an optimal balance between reliably isolating faults and maximizing power supply to non-faulty areas. Isolation based on this target isolation range effectively prevents fault propagation and creates clear and optimized network boundary conditions for rapid power restoration. Compared with existing technologies, this invention improves the accuracy of isolation range delineation after a fault occurs, thereby enhancing fault self-healing capabilities.

[0074] like Figure 5 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a fault isolation system for power Internet of Things (IoT) devices, comprising: The acquisition module 100 is used to acquire electrical signals of each power device in the power Internet of Things, and determine the faulty device and the fault location based on each electrical signal; The processing module 200 is used to determine the number of first and second connected paths corresponding to the faulty device and each faulty connected node based on the power topology diagram, determine the isolation boundary node set for isolating the faulty device based on the number of first connected paths and each of the second connected paths, and calculate the change in the connected paths of each node in the power Internet of Things after simulating the disconnection of the isolation boundary node set, wherein each faulty connected node is determined based on the fault location. The isolation module 300 is used to adjust the isolation boundary node set based on the comparison results of the change amount of each connected path with a preset threshold, so as to obtain a target isolation range that meets the preset conditions, and to isolate the power equipment within the target isolation range.

[0075] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the fault isolation method for power Internet of Things devices provided by any of the above-described method embodiments of the present invention.

[0076] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0077] Based on the above-described embodiments of the fault isolation method for power Internet of Things (IoT) devices, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the fault isolation method for power IoT devices according to any embodiment of the present invention.

[0078] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0079] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0080] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0081] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the fault isolation method for power Internet of Things devices described in any of the above-described method embodiments of the present invention.

[0082] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0083] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A fault isolation method for power Internet of Things (IoT) devices, characterized in that, include: Acquire electrical signals from each power device in the power Internet of Things, and determine the faulty device and fault location based on each electrical signal; Based on the power topology diagram, the number of first and second connected paths corresponding to the faulty equipment and each faulty connected node are determined respectively. Based on the number of first connected paths and each of the second connected paths, an isolation boundary node set for isolating the faulty equipment is determined, and the change in the connected paths of each node in the power Internet of Things after simulating disconnection of the isolation boundary node set is calculated. Each faulty connected node is determined based on the fault location. Based on the comparison results of the changes in each of the connected paths with the preset threshold, the isolation boundary node set is adjusted to obtain a target isolation range that meets the preset conditions, and the power equipment within the target isolation range is isolated.

2. The fault isolation method for power Internet of Things (IoT) devices according to claim 1, characterized in that, The process of determining the faulty device and its location based on the aforementioned electrical signals includes: The rate of change of the signal at each node between adjacent sampling points is calculated based on the electrical signal. When the rate of change of the signal exceeds a preset rate of change threshold, an abnormal band signal is identified, and the signal amplitude deviation and phase offset in the abnormal band signal are extracted as fault features. The fault characteristics are matched with the device characteristics in the power topology diagram to determine the faulty device and the location of the fault.

3. The fault isolation method for power Internet of Things (IoT) devices according to claim 1, characterized in that, The determination of the number of first and second connected paths corresponding to the faulty equipment and each faulty connected node based on the power topology diagram includes: Based on the fault location, a fault area is delineated in the power topology diagram, and several fault connection nodes with power connection to the faulted equipment are identified in the fault area. The number of first connected paths originating from the faulty device is calculated using graph theory algorithms, and the number of second connected paths originating from each of the faulty connected nodes is calculated.

4. The fault isolation method for power Internet of Things (IoT) devices according to claim 1, characterized in that, The step of determining the isolation boundary node set for isolating the faulty device based on the number of the first connected paths and the number of each of the second connected paths includes: Calculate the ratio of the number of second connected paths to the number of first connected paths to obtain the path connectivity ratio of each node; If the connectivity ratio of each path is greater than a preset threshold, the corresponding faulty connectivity nodes are aggregated to determine the isolation boundary node set used to isolate the faulty device.

5. The fault isolation method for power Internet of Things (IoT) devices according to claim 1, characterized in that, The calculation simulation of the change in connectivity paths of each node in the power Internet of Things after disconnecting the isolated boundary node set includes: Based on the isolated boundary node set, the disconnection of the isolated boundary node set is simulated in the network connectivity model to obtain simulation results, wherein the network connectivity model is constructed based on the power topology diagram; Based on the simulation results, the number of third connected paths for each node is calculated; Based on the number of the third and fourth connected paths, the change in connected paths for each node is calculated, wherein the number of the fourth connected paths is calculated for each node in the network connectivity model.

6. The fault isolation method for power Internet of Things (IoT) devices according to claim 1, characterized in that, The adjustment of the isolation boundary node set based on the comparison results of the changes in each of the connected paths and preset thresholds to obtain a target isolation range that meets preset conditions includes: When the change in the connected path is greater than the preset threshold, the node corresponding to the isolated boundary node set is taken as the risk boundary node, and the risk boundary node is removed from the isolated boundary node set to obtain the first processing result. When the changes in the connectivity path are all less than or equal to the preset threshold and the faulty device is not completely isolated, at least one adjacent node that is closest to the faulty device in electrical distance is added to update the isolation boundary node set, and a second processing result is obtained. The target isolation range is determined based on the first processing result and the second processing result until the first processing result and the second processing result meet the preset conditions.

7. The fault isolation method for power Internet of Things (IoT) devices according to any one of claims 1-6, characterized in that, The isolation of power equipment within the target isolation range includes: sending an isolation command containing a device identifier and a disconnection operation to all power equipment within the target isolation range, so as to control the target power equipment corresponding to the device identifier to perform a disconnection operation.

8. A fault isolation system for power Internet of Things (IoT) devices, characterized in that, include; The acquisition module is used to acquire electrical signals of each power device in the power Internet of Things, and determine the faulty device and fault location based on each electrical signal; The processing module is used to determine the number of first and second connected paths corresponding to the faulty device and each faulty connected node based on the power topology diagram; to determine the isolation boundary node set for isolating the faulty device based on the number of first connected paths and each of the second connected paths; and to calculate the change in the connected paths of each node in the power Internet of Things after simulating the disconnection of the isolation boundary node set, wherein each faulty connected node is determined based on the fault location. An isolation module is used to adjust the isolation boundary node set based on the comparison results of the change amount of each connected path with a preset threshold, so as to obtain a target isolation range that meets the preset conditions, and to isolate the power equipment within the target isolation range.

9. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the fault isolation method for power Internet of Things devices as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the steps of the fault isolation method for a power Internet of Things device as described in any one of claims 1-7.

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