Distribution network self-healing method, system, equipment and medium based on fault evolution path

Abstract

The present invention application discloses a distribution network self-healing method, system, equipment and medium based on the fault evolution path. By acquiring the operating data of the target distribution network in real time and collecting the abnormal signal set when the fault occurs, when at least one signal value exceeds the threshold range, combined with the topological structure of the distribution network for analysis, the starting node of the fault propagation path can be accurately determined, laying the foundation for the subsequent steps of accurately dividing the initial fault isolation area; when it is detected that the fault range exceeds the initial fault isolation area, the topological structure is updated based on the real-time load data of the target distribution network, and the evolution of the fault propagation path is predicted, so as to determine the second control node set for further optimization of the fault isolation area of ​​the distribution network and perform self-healing operations. Compared with the existing self-healing strategies, it can timely adjust and optimize the isolation area based on the fault evolution situation, and timely and accurately control the impact of fault evolution.

Description

Technical Field

[0001] The present invention relates to the field of distribution network self-healing, and in particular to a distribution network self-healing method, system, equipment and medium based on a fault evolution path. Background Art

[0002] The distribution network is an important component of the power system. It undertakes the core tasks of power distribution and power supply to users. The stable and reliable operation of the distribution network is directly related to the social and economic operation and residents' lives. Especially in the face of sudden failures, the self-healing ability of the distribution network becomes the key to ensuring power supply continuity.

[0003] However, current distribution network self-healing strategies mostly rely on passive response and post-fault repair. In this context, there is a lack of effective and timely proactive intervention in the early stages of a fault, which makes it difficult to limit the scope of the fault's impact. As the fault further evolves, due to the lack of means to analyze, guide, and adjust the fault path, the power system's isolation of the fault area is less accurate, leading to further resource loss and extended recovery time. Summary of the Invention

[0004] The present invention application provides a distribution network self-healing method, system, device and medium based on the fault evolution path to solve the technical problem of how to improve the accuracy of fault area isolation.

[0005] In order to solve the above technical problems, the present invention provides a distribution network self-healing method based on a fault evolution path, comprising:

[0006] Acquire operating data of the target distribution network in real time, and collect an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes a plurality of signal values;

[0007] When at least one of the signal values ​​exceeds a preset threshold range, obtaining a current topology of the target distribution network; and determining a starting node of a fault propagation path by analyzing the topology and the abnormal signal set;

[0008] Obtaining the switch status of the neighboring nodes according to the position of the starting node; and then obtaining a first control node set based on the switch status of the neighboring nodes;

[0009] Dividing an initial fault isolation area based on the first control node set;

[0010] When it is detected that the fault range exceeds the initial fault isolation area, the topology structure is updated based on the real-time load data of the target distribution network; and the fault propagation path is evolved and predicted based on the updated topology structure to obtain a second control node set; through the second control node set, an optimized fault isolation area is divided, and the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

[0011] As a preferred solution, the updating of the topology structure based on the real-time load data of the target distribution network; and performing an evolution prediction of the fault propagation path based on the updated topology structure to obtain a second control node set include:

[0012] Obtaining node loads of key nodes in a potential fault extension area from the real-time load data;

[0013] Marking the key nodes whose node load is greater than a preset load threshold as risk nodes, and updating the topology structure according to the risk nodes;

[0014] Acquire historical fault data; predict the fault propagation path based on the updated topology and the risk nodes using a preset path evolution prediction model; and predict the fault propagation probability based on the updated topology and the risk nodes using a dynamic Bayesian network algorithm;

[0015] Based on the predicted propagation path and fault propagation probability, a second control node set is obtained through analysis.

[0016] As a preferred solution, the second control node set is used to divide the optimized fault isolation area, and the target distribution network is controlled to perform a self-healing operation based on the optimized fault isolation area, including:

[0017] Analyzing historical operation records and response delays at each second control node in the second control node set, the historical operation records including monthly operation counts;

[0018] Based on the monthly number of operations, response delay, preset time window limit and monthly operation upper limit, a comprehensive analysis is performed to obtain the operation priority of each second control node;

[0019] An optimized fault isolation area is obtained by dividing the second control node set; and according to the operation priority and the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

[0020] As a preferred solution, the controlling the target distribution network to perform a self-healing operation based on the optimized fault isolation area according to the operation priority and in combination with the load distribution state of the optimized fault isolation area includes:

[0021] Obtain resource consumption and load transfer status of the switch operations corresponding to each second control node;

[0022] generating a switching operation plan with the goal of minimizing resource consumption based on the resource consumption and load transfer conditions in combination with the operation priority, thereby obtaining a switching operation sequence;

[0023] Based on the switch operation sequence, in combination with a preset parallel task processing sequence and a load distribution state of the optimized fault isolation area, the target distribution network is controlled to perform a self-healing operation based on the optimized fault isolation area.

[0024] As a preferred solution, dividing the initial fault isolation area based on the first control node set includes:

[0025] Constructing a load constraint of the target distribution network and an operation constraint of the first control node set, wherein the operation constraint includes a resource consumption constraint and an operation frequency constraint;

[0026] According to the load constraint, resource consumption constraint and operation frequency constraint, a preliminary switching action sequence is obtained based on a first control point set;

[0027] An initial fault isolation area is divided according to the preliminary switching action sequence.

[0028] As a preferred solution, obtaining the first control node set based on the switch status of the neighboring nodes includes:

[0029] Based on the switch status of the neighboring nodes, the connection status of all nodes of the target distribution network is analyzed by a graph traversal algorithm to obtain a node connection path set and a state abnormality pattern associated with the neighboring nodes;

[0030] Cluster analysis is performed based on the node connection path set and state anomaly pattern to obtain the topological anomaly subset;

[0031] A first control node set is obtained by dividing the topology abnormal subset.

[0032] As a preferred solution, the determining the starting node of the fault propagation path by analyzing the topology structure and the abnormal signal set includes:

[0033] Using a depth-first search algorithm to traverse the topological structure and obtain a traversal result;

[0034] Filtering out traversal paths whose signal values ​​exceed a preset threshold range from the traversal results;

[0035] The short circuit risk of each node in the traversal path is calculated using the Dijkstra algorithm, and the starting point of the path where the short circuit risk is greater than a preset risk value is used as the starting node of the fault propagation path.

[0036] Accordingly, the present invention also provides a distribution network self-healing system based on the fault evolution path, comprising an acquisition module, a node determination module, a control point determination module, an area division module and a self-healing control module; wherein,

[0037] The acquisition module is used to acquire the operating data of the target distribution network in real time, and to collect an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes a plurality of signal values;

[0038] The node determination module is configured to obtain a current topology of the target distribution network when at least one signal value exceeds a preset threshold range; and determine a starting node of a fault propagation path by analyzing the topology and the abnormal signal set;

[0039] The control point determination module is configured to obtain the switch status of the neighboring nodes according to the position of the starting node; and further obtain the first control node set based on the switch status of the neighboring nodes;

[0040] The area division module is configured to divide the initial fault isolation area based on the first control node set;

[0041] The self-healing control module is configured to update the topology structure based on the real-time load data of the target distribution network when it is detected that the fault range exceeds the initial fault isolation area; and to predict the evolution of the fault propagation path based on the updated topology structure to obtain a second control node set; and to divide the optimized fault isolation area through the second control node set, and to control the target distribution network to perform self-healing operations based on the optimized fault isolation area.

[0042] As a preferred solution, the self-healing control module updates the topology structure based on the real-time load data of the target distribution network; and performs an evolution prediction on the fault propagation path based on the updated topology structure to obtain a second control node set, including:

[0043] The self-healing control module obtains node loads of key nodes in a potential fault extension area from the real-time load data;

[0044] Marking the key nodes whose node load is greater than a preset load threshold as risk nodes, and updating the topology structure according to the risk nodes;

[0045] Acquire historical fault data; predict the fault propagation path based on the updated topology and the risk nodes using a preset path evolution prediction model; and predict the fault propagation probability based on the updated topology and the risk nodes using a dynamic Bayesian network algorithm;

[0046] Based on the predicted propagation path and fault propagation probability, a second control node set is obtained through analysis.

[0047] As a preferred solution, the self-healing control module divides the second control node set to obtain an optimized fault isolation area, and controls the target distribution network to perform a self-healing operation based on the optimized fault isolation area, including:

[0048] The self-healing control module analyzes historical operation records and response delays at each second control node in the second control node set, wherein the historical operation records include monthly operation times;

[0049] Based on the monthly number of operations, response delay, preset time window limit and monthly operation upper limit, a comprehensive analysis is performed to obtain the operation priority of each second control node;

[0050] An optimized fault isolation area is obtained by dividing the second control node set; and according to the operation priority and the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

[0051] As a preferred solution, the self-healing control module controls the target distribution network to perform a self-healing operation based on the optimized fault isolation area according to the operation priority and the load distribution state of the optimized fault isolation area, including:

[0052] The self-healing control module obtains resource consumption and load transfer status of the switch operations corresponding to each second control node;

[0053] generating a switching operation plan with the goal of minimizing resource consumption based on the resource consumption and load transfer conditions in combination with the operation priority, thereby obtaining a switching operation sequence;

[0054] Based on the switch operation sequence, in combination with a preset parallel task processing sequence and a load distribution state of the optimized fault isolation area, the target distribution network is controlled to perform a self-healing operation based on the optimized fault isolation area.

[0055] As a preferred solution, the area division module divides the initial fault isolation area based on the first control node set, including:

[0056] The area division module constructs the load constraints of the target distribution network and the operation constraints of the first control node set, wherein the operation constraints include resource consumption constraints and operation frequency constraints;

[0057] According to the load constraint, resource consumption constraint and operation frequency constraint, a preliminary switching action sequence is obtained based on a first control point set;

[0058] An initial fault isolation area is divided according to the preliminary switching action sequence.

[0059] As a preferred solution, the control point determination module obtains a first control node set based on the switch status of adjacent nodes, including:

[0060] The control point determination module analyzes the connection status of all nodes of the target distribution network based on the switch status of the neighboring nodes through a graph traversal algorithm to obtain a node connection path set and a state abnormality pattern associated with the neighboring nodes;

[0061] Cluster analysis is performed based on the node connection path set and state anomaly pattern to obtain the topological anomaly subset;

[0062] A first control node set is obtained by dividing the topology abnormal subset.

[0063] As a preferred solution, the node determination module determines the starting node of the fault propagation path by analyzing the topology structure and the abnormal signal set, including:

[0064] The node determination module uses a depth-first search algorithm to traverse the topological structure and obtain a traversal result;

[0065] Filtering out traversal paths whose signal values ​​exceed a preset threshold range from the traversal results;

[0066] The short circuit risk of each node in the traversal path is calculated using the Dijkstra algorithm, and the starting point of the path where the short circuit risk is greater than a preset risk value is used as the starting node of the fault propagation path.

[0067] Correspondingly, the present application also 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, and when the processor executes the computer program, it implements the distribution network self-healing method based on the fault evolution path.

[0068] Accordingly, the present application also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network self-healing method based on the fault evolution path.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention provides a distribution network self-healing method, system, device and medium based on a fault evolution path, including: real-time acquisition of operating data of a target distribution network, and collection of an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes several signal values; when at least one of the signal values ​​exceeds a preset threshold range, the current topology of the target distribution network is acquired; and by analyzing the topology and the abnormal signal set, the starting node of the fault propagation path is determined; according to the position of the starting node, the switch status of the adjacent nodes is acquired; and then based on the switch status of the adjacent nodes, a first control node set is obtained; based on the first control node set, an initial fault isolation area is divided; when it is detected that the fault range exceeds the initial fault isolation area, the topology is updated based on the real-time load data of the target distribution network; based on the updated topology, the fault propagation path is predicted to evolve and a second control node set is acquired; through the second control node set, an optimized fault isolation area is obtained, and based on the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations. The present invention obtains the operating data of the target distribution network in real time, collects the abnormal signal set when the fault occurs, and when at least one signal value exceeds the threshold range, combines the topological structure of the distribution network for analysis, and can accurately determine the starting node of the fault propagation path, laying the foundation for the subsequent steps to accurately divide the initial fault isolation area; when it is detected that the fault range exceeds the initial fault isolation area, the topological structure is updated based on the real-time load data of the target distribution network, and the evolution of the fault propagation path is predicted, so as to determine the second control node set for further optimization of the fault isolation area of ​​the target distribution network and perform self-healing operations. Compared with the existing "passive response or post-repair" self-healing strategy, the present invention can timely adjust and optimize the isolation area based on the fault evolution, timely and accurately control the impact of fault evolution, reduce the scope and duration of fault impact, effectively improve the reliability and safety of the target distribution network operation, and enhance the self-healing capability of the target distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 : A flow chart of an embodiment of a distribution network self-healing method based on a fault evolution path provided in the present invention.

[0072] Figure 2 : A flow chart of a preferred implementation mode 1 of an embodiment of a distribution network self-healing method based on a fault evolution path provided in the present application.

[0073] Figure 3: A flow chart of a preferred implementation mode 2 of an embodiment of a distribution network self-healing method based on a fault evolution path provided in the present invention.

[0074] Figure 4 : A flow chart of a preferred implementation mode three of an embodiment of a distribution network self-healing method based on a fault evolution path provided in the present invention.

[0075] Figure 5 : A flow chart of a preferred implementation mode 4 of an embodiment of a distribution network self-healing method based on a fault evolution path provided in the present invention.

[0076] Figure 6 : A structural diagram of an embodiment of a distribution network self-healing system based on a fault evolution path provided in the present invention application. DETAILED DESCRIPTION

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0078] Example 1

[0079] Please refer to Figure 1 , Figure 1 This is a flow chart of an embodiment of a distribution network self-healing method provided by the present invention based on a fault evolution path.

[0080] The distribution network self-healing method based on the fault evolution path described in this embodiment can be applied to the control system of the distribution network, and the control system can be applied to computer devices, including but not limited to smartphones, laptops, tablets, desktop computers, as well as physical servers and cloud servers.

[0081] The distribution network self-healing method based on the fault evolution path described in this embodiment includes steps S101 to S105; each step is described in detail as follows:

[0082] Step S101 : acquiring operating data of a target distribution network in real time, and collecting an abnormal signal set when a fault occurs from the operating data.

[0083] In this embodiment, the operating data includes but is not limited to current data, voltage data, and switch status data.

[0084] When it is determined that a fault has occurred in the target distribution network, this embodiment can obtain operating status information including abnormal voltage fluctuations, abnormal current values ​​and abnormal switch states from multiple data acquisition sources through the distribution network data acquisition system to obtain the above-mentioned abnormal signal set.

[0085] The abnormal signal set includes several signal values, specifically the abnormal voltage fluctuation, abnormal current value and duration of the abnormal switch state.

[0086] Step S102: When at least one of the signal values ​​exceeds a preset threshold range, the current topology of the target distribution network is obtained; and the starting node of the fault propagation path is determined by analyzing the topology and the abnormal signal set.

[0087] In this step, when at least one of the signal values ​​exceeds the preset threshold range, for example, the abnormal voltage fluctuation is greater than the preset voltage fluctuation threshold, the abnormal current value is greater than the preset current threshold, or the duration of the abnormal switch state is greater than the preset time threshold, the current topology of the target distribution network can be obtained.

[0088] Furthermore, the above topological structure can be represented by a directed graph consisting of nodes and edges.

[0089] like Figure 2 As shown, in a preferred embodiment, step S102 determines the starting node of the fault propagation path by analyzing the topology structure and the abnormal signal set, including steps S201 to S203; each step is described in detail as follows:

[0090] Step S201: traverse the topological structure using a depth-first search algorithm to obtain a traversal result.

[0091] Step S202: Filter out traversal paths whose signal values ​​exceed a preset threshold range from the traversal results.

[0092] Step S203 , calculating the short circuit risk of each node in the traversal path by using the Dijkstra algorithm, and taking the starting point of the path with a short circuit risk greater than a preset risk value as the starting node of the fault propagation path.

[0093] For example, if the abnormal signal corresponds to node A, the voltage differences between nodes B, C, and D can be calculated. For example, if the voltage at node A is 240V and there is an abnormal signal at that node, the voltage at node B is 235V, with a 5V difference from node A. The voltage at node C is 245V, with a 5V difference from point A. The voltage at node D is 220V, with a 20V difference from node A. Because the abnormal voltage amplitude at node C is relatively large, node C is determined to be the primary propagation direction. A depth-first search algorithm is then executed to traverse the topology structure according to this rule to obtain the traversal results.

[0094] Assume that multiple traversal paths with signal values ​​exceeding the preset threshold range are traversed and screened out by the above method, one of which is node A-node C-node F. The propagation path between node A and node F can be calculated by the weighted shortest path algorithm (Dijkstra algorithm), and the weight can be based on the impedance of the line (for example, the impedance of line AF is 0.5Ω), and the total path impedance is 1.2Ω. It can be further confirmed that node A-node C-node F is the main fault propagation path, or analysis shows that node A-node E-node F in another traversal path is the main fault propagation path. At this time, the short circuit risk of each node in the main fault propagation path is calculated, and the starting point of the path with a short circuit risk greater than the preset risk value is used as the starting node of the fault propagation path.

[0095] Among them, the calculation method of node short-circuit risk can adopt existing technical methods, such as the current value of a node (95A, exceeding the threshold of 25A) and the switch state (changed 3 times within 1 hour, the threshold is 2 times), and inferring that the node may have a short-circuit risk.

[0096] Step S103: acquiring the switch status of the neighboring nodes according to the position of the starting node; and then obtaining a first control node set based on the switch status of the neighboring nodes.

[0097] As mentioned above, the starting node is obtained by analyzing the fault propagation path. Therefore, when determining the control (node) point, it can be determined by analyzing the adjacent nodes.

[0098] In some preferred embodiments, Figure 3 As shown, step S103 obtains the first control node set based on the switch status of the neighboring nodes, including steps S301 to S303; wherein,

[0099] Step S301 : Based on the switch status of the neighboring nodes, the connection status of all nodes in the target distribution network is analyzed by a graph traversal algorithm to obtain a node connection path set and a state abnormality pattern associated with the neighboring nodes.

[0100] Step S302 : performing cluster analysis based on the node connection path set and the state abnormality pattern to obtain a topology abnormality subset.

[0101] Step S303: obtaining a first control node set by dividing the topology abnormal subset.

[0102] For example, in this preferred embodiment, the starting node determined in step S102 is X. The distributed computing framework can be used to extract neighboring node data centered on node X from the real-time power grid database, including relevant information about five directly connected nodes, Y1, Y2, Y3, Y4, and Y5. For example, the switch status of node Y1 is open, Y2 is closed, Y3 is open, Y4 is closed, and Y5 is closed. At the same time, load data for each node is obtained, such as the load of Y2 is 45.5 kilowatts (normal range 30-50 kilowatts), Y4 is 52.3 kilowatts (overloaded), and Y5 is 38.7 kilowatts.

[0103] Furthermore, based on the aforementioned adjacent nodes and along the paths between nodes, the connection status of all nodes in the target distribution network is analyzed to obtain a set of node connection paths and abnormal status patterns related to the adjacent nodes.

[0104] Different state anomaly patterns are used as classes of the clustering algorithm, and cluster analysis is performed on the node connection path set to obtain topological anomaly subsets, thereby determining the relevant first control nodes and obtaining the first control node set.

[0105] Step S104: Divide the initial fault isolation area based on the first control node set.

[0106] In this embodiment, the first control node (and the second control node described below) can be used to disconnect the faulty area from the normally operating area, preventing the faulty area from extending into the normally operating area of ​​the target distribution network. Therefore, multiple first control nodes can be used to preliminarily determine the initial fault isolation area, laying the foundation for subsequent optimization and refined division of the fault isolation area.

[0107] In a preferred embodiment, Figure 4 As shown, step S104 divides the initial fault isolation area based on the first control node set, including steps S401 to S403; wherein,

[0108] Step S401: constructing the load constraints of the target distribution network and the operation constraints of the first control node set, wherein the operation constraints include resource consumption constraints and operation frequency constraints.

[0109] Step S402 : obtaining a preliminary switching action sequence based on a first control point set according to the load constraint, resource consumption constraint, and operating frequency constraint.

[0110] Step S403: dividing the initial fault isolation area according to the preliminary switch action sequence.

[0111] In this preferred embodiment, based on the constraints of load constraints, resource consumption constraints and operating frequency constraints, combined with the preset objective function, the optimal preliminary switching action sequence can be solved, and then the initial fault isolation area can be gradually divided according to the sequence of switching actions.

[0112] Step S105: When it is detected that the fault range exceeds the initial fault isolation area, the topology structure is updated based on the real-time load data of the target distribution network; and the fault propagation path is predicted to evolve based on the updated topology structure to obtain a second control node set; through the second control node set, an optimized fault isolation area is divided, and the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

[0113] In some preferred embodiments, Figure 5 As shown, step S105 updates the topology structure based on the real-time load data of the target distribution network; and based on the updated topology structure, the fault propagation path is predicted to evolve and a second control node set is obtained, including steps S501 to S504; wherein,

[0114] Step S501: Obtain node loads of key nodes in a potential fault extension area from the real-time load data.

[0115] Step S502: Mark the key nodes whose node load is greater than a preset load threshold as risk nodes, and update the topology structure according to the risk nodes.

[0116] Step S503, obtaining historical fault data; predicting the fault propagation path based on the updated topology structure and the risk nodes using a preset path evolution prediction model; and predicting the fault propagation probability based on the updated topology structure and the risk nodes using a dynamic Bayesian network algorithm.

[0117] Step S504: Based on the predicted propagation path and fault propagation probability, a second control node set is analyzed and obtained.

[0118] In this preferred embodiment, the potential fault expansion area is preset and pre-analyzed before step S501. The topology structure is updated by obtaining the node loads of key nodes in the potential fault expansion area from real-time load data and marking key nodes with a load threshold greater than a preset threshold as risk nodes. Furthermore, based on the updated topology structure and risk nodes, a preset path evolution prediction model is used to predict the fault propagation path, and a dynamic Bayesian network algorithm is used to obtain the fault propagation probability and analyze the second control point set. Compared to existing "passive response or post-repair" self-healing strategies, this can timely adjust and optimize the isolation area based on the fault evolution situation, and timely and accurately control the impact of fault evolution.

[0119] In a preferred embodiment, the optimized fault isolation area is obtained by dividing the second control node set, and the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area, including: analyzing the historical operation records and response delays at each second control node in the second control node set, the historical operation records including the monthly number of operations; performing a comprehensive analysis based on the monthly number of operations, response delay, preset time window limit and monthly operation upper limit to obtain the operation priority of each second control node; the optimized fault isolation area is obtained by dividing the second control node set; and according to the operation priority, combined with the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

[0120] This preferred embodiment performs a comprehensive analysis of the monthly number of operations, response delay, preset time window limit and monthly operation upper limit to obtain the operation priority of each second control node, and then combines the load distribution status of the optimized fault isolation area to control the target distribution network to perform self-healing operations based on the optimized fault isolation area. This can enable the operation priority of the second control node in the self-healing process to comprehensively consider aspects such as response speed and monthly number of operations, further ensuring that the distribution network still has good operating performance during the self-healing process.

[0121] Preferably, according to the operation priority, combined with the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area, including: obtaining the resource consumption and load transfer status of the switch operations corresponding to each second control node; generating a switch operation scheme with the goal of minimizing resource consumption based on the resource consumption and load transfer status combined with the operation priority, and then obtaining a switch operation sequence; based on the switch operation sequence, combined with a preset parallel task processing sequence and the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area. This preferred embodiment can combine the resource consumption, load transfer status and operation priority of the switch operations corresponding to the second control node to generate a switch operation scheme with the goal of minimizing resource consumption, ensure that the loss of the distribution network during the self-healing process is minimized, and ensure stable operation during the self-healing process.

[0122] Accordingly, if Figure 6 As shown, the present invention also provides a distribution network self-healing system 600 based on the fault evolution path, including an acquisition module 601, a node determination module 602, a control point determination module 603, an area division module 604 and a self-healing control module 605; wherein,

[0123] The acquisition module 601 is used to acquire the operating data of the target distribution network in real time, and collect an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes a plurality of signal values;

[0124] The node determination module 602 is configured to obtain a current topology of the target distribution network when at least one of the signal values ​​exceeds a preset threshold range; and determine a starting node of a fault propagation path by analyzing the topology and the abnormal signal set;

[0125] The control point determination module 603 is configured to obtain the switch status of the neighboring nodes according to the position of the starting node; and further obtain the first control node set based on the switch status of the neighboring nodes;

[0126] The area division module 604 is configured to divide the initial fault isolation area based on the first control node set;

[0127] The self-healing control module 605 is used to update the topology structure based on the real-time load data of the target distribution network when it is detected that the fault range exceeds the initial fault isolation area; and to predict the evolution of the fault propagation path based on the updated topology structure to obtain a second control node set; through the second control node set, an optimized fault isolation area is divided, and based on the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations.

[0128] As a preferred solution, the self-healing control module 605 updates the topology structure based on the real-time load data of the target distribution network; and performs an evolution prediction on the fault propagation path based on the updated topology structure to obtain a second control node set, including:

[0129] The self-healing control module 605 obtains the node load of the key node in the potential fault extension area from the real-time load data;

[0130] Marking the key nodes whose node load is greater than a preset load threshold as risk nodes, and updating the topology structure according to the risk nodes;

[0131] Acquire historical fault data; predict the fault propagation path based on the updated topology and the risk nodes using a preset path evolution prediction model; and predict the fault propagation probability based on the updated topology and the risk nodes using a dynamic Bayesian network algorithm;

[0132] Based on the predicted propagation path and fault propagation probability, a second control node set is obtained through analysis.

[0133] As a preferred solution, the self-healing control module 605 divides the second control node set into optimized fault isolation areas, and controls the target distribution network to perform self-healing operations based on the optimized fault isolation areas, including:

[0134] The self-healing control module 605 analyzes historical operation records and response delays at each second control node in the second control node set, wherein the historical operation records include monthly operation times;

[0135] Based on the monthly number of operations, response delay, preset time window limit and monthly operation upper limit, a comprehensive analysis is performed to obtain the operation priority of each second control node;

[0136] An optimized fault isolation area is obtained by dividing the second control node set; and according to the operation priority and the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

[0137] As a preferred solution, the self-healing control module 605 controls the target distribution network to perform a self-healing operation based on the optimized fault isolation area according to the operation priority and the load distribution state of the optimized fault isolation area, including:

[0138] The self-healing control module 605 obtains resource consumption and load transfer status of the switch operations corresponding to each second control node;

[0139] generating a switching operation plan with the goal of minimizing resource consumption based on the resource consumption and load transfer conditions in combination with the operation priority, thereby obtaining a switching operation sequence;

[0140] Based on the switch operation sequence, in combination with a preset parallel task processing sequence and a load distribution state of the optimized fault isolation area, the target distribution network is controlled to perform a self-healing operation based on the optimized fault isolation area.

[0141] As a preferred solution, the area division module 604 divides the initial fault isolation area based on the first control node set, including:

[0142] The area division module 604 constructs the load constraints of the target distribution network and the operation constraints of the first control node set, wherein the operation constraints include resource consumption constraints and operation frequency constraints;

[0143] According to the load constraint, resource consumption constraint and operation frequency constraint, a preliminary switching action sequence is obtained based on a first control point set;

[0144] An initial fault isolation area is divided according to the preliminary switching action sequence.

[0145] As a preferred solution, the control point determination module 603 obtains a first control node set based on the switch status of the neighboring nodes, including:

[0146] The control point determination module 603 analyzes the connection status of all nodes of the target distribution network based on the switch status of the neighboring nodes through a graph traversal algorithm to obtain a node connection path set and a state abnormality pattern associated with the neighboring nodes;

[0147] Cluster analysis is performed based on the node connection path set and state anomaly pattern to obtain the topological anomaly subset;

[0148] A first control node set is obtained by dividing the topology abnormal subset.

[0149] As a preferred solution, the node determination module 602 determines the starting node of the fault propagation path by analyzing the topology structure and the abnormal signal set, including:

[0150] The node determination module 602 uses a depth-first search algorithm to traverse the topology structure and obtain a traversal result;

[0151] Filtering out traversal paths whose signal values ​​exceed a preset threshold range from the traversal results;

[0152] The short circuit risk of each node in the traversal path is calculated using the Dijkstra algorithm, and the starting point of the path where the short circuit risk is greater than a preset risk value is used as the starting node of the fault propagation path.

[0153] Correspondingly, the present application also 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, and when the processor executes the computer program, it implements the distribution network self-healing method based on the fault evolution path.

[0154] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal and connects various parts of the entire terminal using various interfaces and lines.

[0155] The memory can be used to store the computer program. The processor implements the various functions of the terminal by running or executing the computer program stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0156] Accordingly, the present application also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network self-healing method based on the fault evolution path.

[0157] If the integrated module of the distribution network self-healing system based on the fault evolution path is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc.

[0158] Compared with the prior art, the present invention has the following beneficial effects:

[0159] The present invention provides a distribution network self-healing method, system, device and medium based on a fault evolution path, including: real-time acquisition of operating data of a target distribution network, and collection of an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes several signal values; when at least one of the signal values ​​exceeds a preset threshold range, the current topology of the target distribution network is acquired; and by analyzing the topology and the abnormal signal set, the starting node of the fault propagation path is determined; according to the position of the starting node, the switch status of the adjacent nodes is acquired; and then based on the switch status of the adjacent nodes, a first control node set is obtained; based on the first control node set, an initial fault isolation area is divided; when it is detected that the fault range exceeds the initial fault isolation area, the topology is updated based on the real-time load data of the target distribution network; based on the updated topology, the fault propagation path is predicted to evolve and a second control node set is acquired; through the second control node set, an optimized fault isolation area is obtained, and based on the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations. The present invention obtains the operating data of the target distribution network in real time, collects the abnormal signal set when the fault occurs, and when at least one signal value exceeds the threshold range, combines the topological structure of the distribution network for analysis, and can accurately determine the starting node of the fault propagation path, laying the foundation for the subsequent steps to accurately divide the initial fault isolation area; when it is detected that the fault range exceeds the initial fault isolation area, the topological structure is updated based on the real-time load data of the target distribution network, and the evolution of the fault propagation path is predicted, so as to determine the second control node set for further optimization of the fault isolation area of ​​the target distribution network and perform self-healing operations. Compared with the existing "passive response or post-repair" self-healing strategy, the present invention can timely adjust and optimize the isolation area based on the fault evolution, timely and accurately control the impact of fault evolution, reduce the scope and duration of fault impact, effectively improve the reliability and safety of the target distribution network operation, and enhance the self-healing capability of the target distribution network.

[0160] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A distribution network self-healing method based on fault evolution path, characterized in that: include: Acquire operating data of the target distribution network in real time, and collect an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes a plurality of signal values; When at least one of the signal values ​​exceeds a preset threshold range, obtaining a current topology of the target distribution network; and determining a starting node of a fault propagation path by analyzing the topology and the abnormal signal set; Obtaining the switch status of the neighboring nodes according to the position of the starting node; and then obtaining a first control node set based on the switch status of the neighboring nodes; Dividing an initial fault isolation area based on the first control node set; When it is detected that the fault range exceeds the initial fault isolation area, updating the topology structure based on the real-time load data of the target distribution network; And based on the updated topology structure, the fault propagation path is predicted to evolve and the second control node set is obtained; An optimized fault isolation area is obtained by dividing the second control node set, and the target distribution network is controlled to perform a self-healing operation based on the optimized fault isolation area.

2. A distribution network self-healing method based on fault evolution path according to claim 1, characterized in that: updating the topology structure based on the real-time load data of the target distribution network; Based on the updated topology, the fault propagation path is predicted to evolve, and a second set of control nodes is obtained, including: Obtaining node loads of key nodes in a potential fault extension area from the real-time load data; Marking the key nodes whose node load is greater than a preset load threshold as risk nodes, and updating the topology structure according to the risk nodes; Acquire historical fault data; predict the fault propagation path based on the updated topology and the risk nodes using a preset path evolution prediction model; and predict the fault propagation probability based on the updated topology and the risk nodes using a dynamic Bayesian network algorithm; Based on the predicted propagation path and fault propagation probability, a second control node set is obtained through analysis.

3. The distribution network self-healing method based on fault evolution path according to claim 1, characterized in that: The step of dividing the optimized fault isolation area by the second control node set and controlling the target distribution network to perform a self-healing operation based on the optimized fault isolation area includes: Analyzing historical operation records and response delays at each second control node in the second control node set, the historical operation records including monthly operation counts; Based on the monthly operation number, response delay, preset time window limit and monthly operation upper limit, a comprehensive analysis is performed to obtain the operation priority of each second control node; An optimized fault isolation area is obtained by dividing the second control node set; and according to the operation priority and the load distribution status of the optimized fault isolation area, the target distribution network is controlled to perform self-healing operations based on the optimized fault isolation area.

4. A distribution network self-healing method based on fault evolution path according to claim 3, characterized in that: The controlling the target distribution network to perform a self-healing operation based on the optimized fault isolation area according to the operation priority and in combination with the load distribution state of the optimized fault isolation area includes: Obtain resource consumption and load transfer status of the switch operations corresponding to each second control node; generating a switching operation plan with the goal of minimizing resource consumption based on the resource consumption and load transfer conditions in combination with the operation priority, thereby obtaining a switching operation sequence; Based on the switch operation sequence, in combination with a preset parallel task processing sequence and a load distribution state of the optimized fault isolation area, the target distribution network is controlled to perform a self-healing operation based on the optimized fault isolation area.

5. The distribution network self-healing method based on fault evolution path according to claim 1, characterized in that: The dividing the initial fault isolation area based on the first control node set includes: Constructing a load constraint of the target distribution network and an operation constraint of the first control node set, wherein the operation constraint includes a resource consumption constraint and an operation frequency constraint; According to the load constraint, resource consumption constraint and operation frequency constraint, a preliminary switching action sequence is obtained based on the first control node set; An initial fault isolation area is divided according to the preliminary switching action sequence.

6. A distribution network self-healing method based on fault evolution path according to claim 1, characterized in that: The obtaining of a first control node set based on the switch status of the neighboring nodes includes: Based on the switch status of the neighboring nodes, the connection status of all nodes of the target distribution network is analyzed by a graph traversal algorithm to obtain a node connection path set and a state abnormality pattern associated with the neighboring nodes; Cluster analysis is performed based on the node connection path set and state anomaly pattern to obtain the topological anomaly subset; A first control node set is obtained by dividing the topology abnormal subset.

7. The distribution network self-healing method based on fault evolution path according to claim 1, characterized in that: The determining the starting node of the fault propagation path by analyzing the topology structure and the abnormal signal set includes: Using a depth-first search algorithm to traverse the topological structure and obtain a traversal result; Filtering out traversal paths whose signal values ​​exceed a preset threshold range from the traversal results; The short circuit risk of each node in the traversal path is calculated using the Dijkstra algorithm, and the starting point of the path where the short circuit risk is greater than a preset risk value is used as the starting node of the fault propagation path.

8. A distribution network self-healing system based on fault evolution path, characterized in that: It includes acquisition module, node determination module, control point determination module, area division module and self-healing control module; among them, The acquisition module is used to acquire the operating data of the target distribution network in real time, and to collect an abnormal signal set when a fault occurs from the operating data; the abnormal signal set includes a plurality of signal values; The node determination module is configured to obtain a current topology of the target distribution network when at least one signal value exceeds a preset threshold range; and determine a starting node of a fault propagation path by analyzing the topology and the abnormal signal set; The control point determination module is configured to obtain the switch status of the neighboring nodes according to the position of the starting node; and further obtain the first control node set based on the switch status of the neighboring nodes; The area division module is configured to divide the initial fault isolation area based on the first control node set; The self-healing control module is configured to update the topology structure based on the real-time load data of the target distribution network when it is detected that the fault range exceeds the initial fault isolation area; and to predict the evolution of the fault propagation path based on the updated topology structure to obtain a second control node set; and to divide the optimized fault isolation area through the second control node set, and to control the target distribution network to perform self-healing operations based on the optimized fault isolation area.

9. A terminal device, characterized in that: The method comprises 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, the method for self-healing a distribution network based on a fault evolution path according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network self-healing method based on the fault evolution path according to any one of claims 1 to 7.

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