Power distribution network protection and self-healing method and system based on overhead line

Abstract

The invention relates to the technical field of power grid operation control, and discloses a power distribution network protection and self-healing method and system based on an overhead line, and the method comprises the steps: obtaining first data and second data of a target regional power grid; establishing a target area distribution network structure according to a plurality of data in the first data; establishing a first self-healing strategy based on the first data, the second data and a target area distribution network structure; and performing self-healing on the power distribution network of the overhead line according to the first self-healing strategy. The method takes the target person as the core, and preferentially guarantees the power supply demand of the target person; all possible fault conditions are comprehensively considered by calculating fault indexes from factors such as geography, weather, fault rate and time; the weather factors are added, the problem that the maintenance time is prolonged due to the weather factors, and the fault time is prolonged is solved, a single consideration factor is not emphasized, a self-healing strategy is obtained through multi-dimensional analysis, and the strategy is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation control, and in particular to a distribution network protection and self-healing method and system based on overhead lines. Background Art

[0002] With the development of technologies such as distribution automation, distribution fault self-healing technology has emerged. Specifically, when the relay protection trips the switch (if the reclosing switch is activated, the reclosing action fails after the relay protection trips the switch, and the switch trips again), the fault self-healing system (which can be implemented by the distribution automation system) first determines the fault area, isolates the fault interval, and then restores power supply to the non-fault area by closing the interconnecting switch, thereby realizing fault self-healing.

[0003] With the rapid development of modern society and the growing demand for electricity, current fault self-healing systems typically employ communication-based automation or distributed intelligent control. When a distribution network fault occurs, the distribution automation master station intelligently identifies the faulty area by analyzing signals from integrated primary and secondary intelligent terminals or peer-to-peer communication. It then remotely controls circuit breakers to isolate the faulty area and restore power to non-faulty areas. This approach enables rapid fault location and isolation, significantly reducing troubleshooting time.

[0004] However, the above method only considers the internal factors of the power equipment to minimize the scope of the fault. However, the fault area may be expanded due to various external reasons. For example, stormy weather may cause tree branches to hang on the line. If the line is energized, it may cause a short circuit, thereby expanding the scope of the fault. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a distribution network protection and self-healing method and system based on overhead lines, which can solve the problem that the fault range may be expanded due to external reasons.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a distribution network protection and self-healing method based on overhead lines, comprising:

[0009] Acquiring first data and second data of a target area power grid;

[0010] Establishing a target area distribution network structure according to some data in the first data;

[0011] Establishing a first self-healing strategy based on the first data, the second data, and the target area distribution network structure;

[0012] The first self-healing strategy includes:

[0013] Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data;

[0014] Acquire a first topology map according to the target area distribution network structure and the second data;

[0015] Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map;

[0016] Establishing a first self-healing strategy according to the second topology map;

[0017] The distribution network of the overhead line is self-healed according to the first self-healing strategy.

[0018] As a preferred solution of the overhead line-based distribution network protection and self-healing method of the present invention, the obtaining of the first fault indicator according to the first data includes:

[0019] The first failure indicator includes a first failure rate, a second failure rate, a recovery rate, a recovery complexity, and a recovery time;

[0020] Acquire a fault event according to the first data, and acquire a fault order of the corresponding fault event in combination with the target area distribution network structure;

[0021] A corresponding first failure rate is obtained according to the failure order.

[0022] As a preferred solution of the overhead line-based distribution network protection and self-healing method of the present invention, the step of obtaining the first fault indicator according to the first data further comprises:

[0023] Acquire fault data of the fault point according to the first data, wherein the fault data includes fault frequency, fault type, fault location, maintenance data, equipment data, and fault range;

[0024] obtaining a first fault index based on the fault frequency and the fault location;

[0025] Obtaining a service life of the equipment based on the equipment data, and obtaining a second fault index based on the service life of the equipment;

[0026] The second failure rate is obtained based on the first failure index and the second failure index.

[0027] As a preferred solution of the overhead line-based distribution network protection and self-healing method of the present invention, the step of obtaining the first fault indicator according to the first data further comprises:

[0028] Obtaining the recovery rate based on the fault type and the maintenance data;

[0029] Obtaining the restoration complexity based on the maintenance data and the fault scope;

[0030] The restoration time is obtained based on the maintenance data and the maintenance preparation time.

[0031] As a preferred solution of the overhead line-based distribution network protection and self-healing method of the present invention, the first data and the second data include:

[0032] The first data includes historical fault data, distribution network data, geographic data, weather data and first target personnel data;

[0033] The second data includes new fault location and new fault signal data.

[0034] As a preferred solution of the overhead line-based distribution network protection and self-healing method of the present invention, the third weather index includes:

[0035] Obtaining a weather type and duration based on the weather data, and obtaining abnormal time consumption based on the fault type, the maintenance data, the weather type and the duration;

[0036] Obtaining a normal time consumption based on the fault type and the maintenance data;

[0037] The third weather index is obtained based on the abnormal time consumption and the normal time consumption.

[0038] As a preferred solution of the overhead line-based distribution network protection and self-healing method of the present invention, the first self-healing strategy further includes:

[0039] Obtaining a key node based on the target area distribution network structure and the second key area;

[0040] Obtaining a key line based on the key node and the new fault location;

[0041] Obtaining a first topology map based on the key line and the new fault signal data;

[0042] Obtaining a comprehensive index based on the first fault index and the third weather index;

[0043] Based on the comprehensive indicator and the distribution network structure, the first topology map is screened to obtain a second topology map;

[0044] Based on the second topology graph, the first self-healing strategy is obtained.

[0045] In a second aspect, the present invention provides a distribution network protection and self-healing system based on overhead lines, comprising:

[0046] A data acquisition module, configured to acquire first data and second data of a target area power grid;

[0047] a structure establishing module, configured to establish a target area distribution network structure according to a plurality of data in the first data;

[0048] A strategy establishment module, configured to establish a first self-healing strategy based on the first data, the second data, and the target area distribution network structure;

[0049] The first self-healing strategy includes:

[0050] Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data;

[0051] Acquire a first topology map according to the target area distribution network structure and the second data;

[0052] Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map;

[0053] Establishing a first self-healing strategy according to the second topology map;

[0054] The self-healing module is configured to perform self-healing on the distribution network of the overhead line according to the first self-healing strategy.

[0055] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-described method when executing the computer program.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0057] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention proposes a distribution network protection and self-healing method and system based on overhead lines, obtains first data and second data of the target area power grid; establishes the target area distribution network structure based on several data in the first data; establishes a first self-healing strategy based on the first data, the second data and the target area distribution network structure; and self-heals the distribution network of the overhead lines according to the first self-healing strategy. The present invention takes the target personnel as the core and gives priority to ensuring the power supply needs of the target personnel; comprehensively considers all possible fault conditions by calculating fault indicators from factors such as geography, weather, failure rate and time; incorporates weather factors to reduce the problem of extended maintenance time due to weather factors, which leads to extended fault time. It does not focus on a single consideration factor, but obtains a self-healing strategy through multi-dimensional analysis, making the strategy more complete. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0059] Figure 1 A method flow chart of a distribution network protection and self-healing method based on overhead lines provided in one embodiment of the present invention.

[0060] Figure 2 An internal structural diagram of a computer device for a distribution network protection and self-healing method based on overhead lines provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0061] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0062] Example 1, with reference to Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a distribution network protection and self-healing method based on overhead lines, comprising:

[0063] Existing technologies have several issues. For example, fault recovery systems only consider internal factors within power equipment, failing to fully account for external factors that could cause a wider range of faults. This is particularly true in severe weather conditions, such as when a storm causes tree branches to become lodged in power lines. If the lines are energized, this could cause a short circuit, further widening the fault's scope.

[0064] This application provides a method that can effectively solve the above-mentioned problems. Next, we will explain in detail how to implement the distribution network protection and self-healing method based on overhead lines in combination with multiple embodiments.

[0065] Figure 1 A method flow chart of a distribution network protection and self-healing method based on overhead lines is shown, including:

[0066] S101, obtaining first data and second data of a target area power grid;

[0067] In an optional embodiment, the target area power grid may be a designated urban area, industrial park, or residential area.

[0068] In an optional embodiment, the first data may include but is not limited to historical fault data, distribution network data, geographic data, weather data and first target personnel data. Historical fault data records key information such as past fault events, fault locations, fault types, maintenance records, etc., which is helpful for analyzing the patterns and trends of fault occurrence. Distribution network data covers the structure, equipment parameters, load distribution, etc. of the distribution network, and is the basis for establishing a distribution network structure model. Geographic data provides information such as the geographical location, topography, and vegetation distribution of the target area, which helps to evaluate the impact of external factors on the distribution network. Weather data contains meteorological information such as temperature, humidity, wind speed, and rainfall, which is crucial for predicting and preventing weather-related failures. The first target personnel data is associated with key users or important facilities to ensure that their power supply needs are prioritized when a fault occurs.

[0069] In this embodiment of the present application, the first data and the second data include:

[0070] The first data includes historical fault data, distribution network data, geographic data, weather data and first target personnel data;

[0071] The second data includes new fault location and new fault signal data.

[0072] In an embodiment of the present application, it is possible to integrate primary and secondary intelligent terminal signals, fault detection information, and peer-to-peer communication between intelligent terminals. Furthermore, the new fault location and new fault signal data in the second data can be obtained in real time or near real time to quickly respond to any new faults occurring in the distribution network. The new fault location data provides the precise geographic location of the fault, while the new fault signal data may include detailed information such as the fault type indication, fault current magnitude, and fault occurrence time. This information is crucial for quickly locating faults, assessing the scope of fault impact, and formulating effective self-healing strategies.

[0073] It should be noted that obtaining the first and second data of the target area power grid can provide a comprehensive understanding of the target area power grid's operating status and historical fault conditions, providing data support for subsequent analysis and the development of self-healing strategies. Historical fault data can be used to analyze the patterns and trends of fault occurrences, identify high-incidence areas and time periods, and provide a scientific basis for fault prevention. Distribution network data helps establish an accurate distribution network structure model, providing a foundation for fault location, isolation, and recovery. The combination of geographic data and weather data can assess the potential impact of external factors on the distribution network and take proactive measures to prevent the expansion of faults caused by external factors such as severe weather. The consideration of the first target personnel data ensures the power supply needs of key users or important facilities in the event of a fault, improving the reliability and stability of the power grid.

[0074] S102, establishing a target area distribution network structure according to some data in the first data;

[0075] In an optional embodiment, the distribution network structure of the target area can be obtained by parsing and modeling distribution network data. Distribution network data contains detailed information such as the distribution network topology, equipment parameters, line lengths, and load distribution. By processing and analyzing this data, a distribution network structure model of the target area can be constructed. This model can intuitively display the layout of the distribution network and equipment connections, providing a foundation for subsequent fault location, isolation, and restoration operations.

[0076] In an optional embodiment, in the process of establishing the distribution network structure model, geographical data factors such as topography, vegetation distribution, etc. can also be considered to more accurately evaluate the impact of external factors on the distribution network.

[0077] In an embodiment of the present application, the distribution network structure of the target area is obtained based on the distribution network data and the geographic data in the first data, the device nodes, line nodes and the connection relationship between the nodes are obtained based on the distribution network data, thereby obtaining a topological structure, the geographic location of the node is obtained based on the geographic data, and the geographic structure is obtained. Based on the topological structure and the geographic structure, the two are combined to obtain the distribution network structure.

[0078] For example, let N = {n1, n2, ..., n k}: The set of all nodes (device nodes and line nodes) in the distribution network. i ,n j )|n i ,n j ∈N}: A set of connection relationships between nodes, where each pair (n i ,n j ) represents node n i With node n j There is a direct connection between them. i )=(x i ,y i ): node n i The geographic coordinates of x i ,y i are the horizontal and vertical coordinates of the node in the geographic coordinate system. T(N,C): The topological structure based on the node set N and its connection relationship set C. G(N,L): The geographic structure based on the node set N and its corresponding geographic location set L.

[0079] Step 1: Construct a topological structure. For a given set of nodes N and a set of connection relationships C between them, this application first establishes a topological structure T(N,C). In this structure, each node n i ∈N are connected to other nodes, and these adjacent nodes are defined by the connection relationship set C.

[0080] T(N,C)={n i →Adj(n i )|n i ∈N}

[0081] Among them, Adj(n i ) represents the node n i The set of all directly connected nodes.

[0082] Step 2: Integrate geographic information. This application integrates the location information of each node into the above topology. The result of this step is a structure G(N,L) that combines electrical connection properties and physical location information.

[0083] G(N,L)={n i →(Adj(n i ),L(n i ))|n i ∈N}

[0084] Here, n i Not only is it associated with its adjacent nodes, but it is also associated with its geographical location L(n i ).

[0085] Step 3: Merge to form the final intelligent distribution network structure. Merge the topological structure T(N,C) and the geographical structure G(N,L) to obtain the final distribution network structure model S. This model includes both the electrical connectivity and physical layout information of the network.

[0086] S={n i →(Adj(n i ),L(n i ))|n i ∈N}

[0087] This means that for each node n in the distribution network i , the application clearly knows which nodes it is connected to (ie Adj(n i )), and its exact geographical location (i.e. L(n i )).

[0088] It should be noted that establishing the target area's distribution network structure based on several of the first data points can provide a clear understanding of the target area's distribution network layout and equipment connections, providing a foundation for subsequent fault location, isolation, and restoration operations. The distribution network structure model can intuitively display the location of each device node and line node in the distribution network and their interconnected relationships, helping operations and maintenance personnel quickly and accurately identify fault points and take effective measures to address them. Furthermore, the distribution network structure model can provide a scientific basis for the development of self-healing strategies, ensuring their effectiveness and feasibility.

[0089] S103: Establish a first self-healing strategy based on the first data, the second data, and the target area distribution network structure;

[0090] It should be noted that the first self-healing strategy is designed to rapidly locate the fault point, assess the impact scope, and automatically implement measures to isolate the fault and restore power when a distribution network fault occurs. In developing this strategy, it is necessary to comprehensively consider historical fault data, distribution network data, geographic data, weather data, and the first target personnel data in the first data set, as well as new fault location and new fault signal data in the second data set. Combined with the distribution network structure in the target area, comprehensive analysis and calculation are performed. Analysis of historical fault data can identify high-prone fault areas and time periods, providing a scientific basis for fault prevention. The combination of distribution network data and geographic data helps accurately assess the impact scope and develop effective fault isolation and restoration plans. Incorporating weather data can proactively predict and prevent the expansion of faults caused by severe weather, thereby enhancing the grid's resilience. The inclusion of the first target personnel data ensures the power supply needs of critical users or important facilities in the event of a fault, improving the reliability and stability of the grid.

[0091] In an optional embodiment, the first self-healing strategy can be formulated using a machine learning algorithm or an artificial intelligence model. These algorithms or models can analyze a large amount of historical fault data, distribution network data, geographic data, weather data, and first target personnel data to learn the patterns and trends of fault occurrence, as well as the weights of different factors on the degree of impact of faults. Based on these learning results, the algorithm or model can quickly locate the fault point, predict the scope of fault impact, and generate the optimal fault isolation and recovery plan when a new fault occurs. These plans may include cutting off power to the fault area, redistributing loads, starting backup power supplies, etc., to ensure that the power supply needs of key users or important facilities are met, while minimizing the impact of the fault on the overall operation of the power grid.

[0092] In an optional embodiment, the first self-healing strategy can also be formulated using a heuristic algorithm or an optimization algorithm. These algorithms can consider multiple constraints in a complex distribution network environment, such as equipment capacity limitations, line transmission capabilities, load priorities, etc., to find the optimal or suboptimal self-healing solution. Through iterative calculations, the algorithm can continuously adjust the fault isolation and power restoration strategy to achieve the goal of minimizing power outage duration and maximizing the amount of restored power. At the same time, these algorithms can also consider changes in real-time weather data and geographic data, dynamically adjusting the self-healing strategy to adapt to the ever-changing external environment.

[0093] It should be noted that the aforementioned first self-healing strategy is based on existing algorithms, whose underlying logic is not well compatible with the complex and changing distribution network environment. In particular, in the face of extreme weather and sudden failures, existing algorithms may not be able to quickly make optimal self-healing decisions. Therefore, this application proposes the following underlying logic for the first self-healing strategy.

[0094] In this embodiment of the present application, the first self-healing strategy includes:

[0095] Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data;

[0096] Acquire a first topology map according to the target area distribution network structure and the second data;

[0097] Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map;

[0098] Establishing a first self-healing strategy according to the second topology map;

[0099] In the embodiment of the present application, obtaining the first fault indicator according to the first data includes:

[0100] The first fault indicator includes a first failure rate, a second failure rate, a recovery rate, a recovery complexity, and a recovery time, and different weights are assigned to them and added together to obtain the fault indicator;

[0101] Acquire a fault event according to the first data, and acquire a fault order of the corresponding fault event in combination with the target area distribution network structure;

[0102] A corresponding first failure rate is obtained according to the failure order.

[0103] In an optional embodiment, a state enumeration method may be used to obtain a fault event based on the historical fault data, obtain a fault order based on the fault event and the topology structure, and obtain a first fault rate based on the fault order.

[0104] In the embodiment of the present application, obtaining the first fault indicator according to the first data further includes:

[0105] Acquire fault data of the fault point according to the first data, wherein the fault data includes fault frequency, fault type, fault location, maintenance data, equipment data, and fault range;

[0106] A first fault index is obtained based on the fault frequency and the fault location, such as obtaining the fault frequency of each fault location and classifying the fault frequencies.

[0107] Obtaining a service life of the equipment based on the equipment data, and obtaining a second fault index based on the service life of the equipment;

[0108] The second failure rate is obtained based on the first failure index and the second failure index. For example, the first failure index and the second failure index are assigned different weights, and the second failure rate is obtained by adding them together.

[0109] In the embodiment of the present application, obtaining the first fault indicator according to the first data further includes:

[0110] Obtaining the recovery rate based on the fault type and the maintenance data;

[0111] Obtaining the restoration complexity based on the maintenance data and the fault scope;

[0112] The restoration time is obtained based on the maintenance data and the maintenance preparation time.

[0113] In an optional embodiment, the recovery rate is obtained based on the fault type and the maintenance data; if the fault type is divided into permanent fault and temporary fault, and the maintenance type is replacement and repair, if the fault is a permanent fault and replacement, the recovery rate is -1; if the fault is a temporary fault and replacement, the recovery rate is -0.5; if the fault is a temporary fault and repair, the recovery rate is 1.

[0114] In an optional embodiment, the recovery complexity is obtained based on the maintenance data and the fault scope. For example, the maintenance time is obtained based on the maintenance data, and different weights are assigned to the maintenance time and the fault scope to obtain the recovery complexity. The maintenance time refers to the time from the start of maintenance to the end of maintenance.

[0115] In an optional embodiment, the recovery time is obtained based on the maintenance data and the maintenance preparation time. If the maintenance time is obtained based on the maintenance data, the recovery time is obtained by adding the maintenance time and the maintenance preparation time. The maintenance preparation time refers to the time it takes for maintenance personnel to travel from the starting point to the fault point.

[0116] In this embodiment of the present application, the first self-healing strategy further includes:

[0117] Obtaining a key node based on the target area distribution network structure and the second key area;

[0118] Obtaining a key line based on the key node and the new fault location;

[0119] Obtaining a first topology map based on the key line and the new fault signal data;

[0120] Obtaining a comprehensive index based on the first fault index and the third weather index;

[0121] Based on the comprehensive indicator and the distribution network structure, the first topology map is screened to obtain a second topology map;

[0122] Based on the second topology graph, the first self-healing strategy is obtained.

[0123] In an optional embodiment, the second key area can be obtained by the following steps:

[0124] First, based on the geographic data, a first coordinate of a first target point is obtained, and based on a preset range, the first coordinate and the second target point, a first area is obtained. For example, the first coordinate of a hospital is obtained, and with the first coordinate as the midpoint, a cell within the preset range is obtained, and the area occupied by the cell is obtained to obtain the first area.

[0125] Secondly, based on the key person data (i.e., the first target person data), a second area is obtained; if the key person is a patient requiring a ventilator at home, the patient's residential area is obtained, the area occupied by the residential area is obtained, and the second area is obtained;

[0126] Finally, based on the first region and the second region, the second key region is obtained. The first region and the second region are added and fused to obtain the second key region.

[0127] In this embodiment of the present application, the third weather index includes:

[0128] Obtaining a weather type and duration based on the weather data, and obtaining abnormal time consumption based on the fault type, the maintenance data, the weather type and the duration;

[0129] Obtaining a normal time consumption based on the fault type and the maintenance data;

[0130] The third weather index is obtained based on the abnormal time consumption and the normal time consumption.

[0131] In an optional embodiment, the third weather index can be obtained by the following steps:

[0132] First, the weather type and duration are obtained based on the weather data, and the abnormal time consumption is obtained based on the fault type, the maintenance data, the weather type and the duration; for example, in stormy weather, the abnormal average time required to repair a damaged circuit breaker is calculated.

[0133] Duration refers to the time from the beginning to the end of weather conditions, such as the time from the beginning to the end of rain.

[0134] In this embodiment, the weather type may be high temperature weather exceeding a preset temperature, rainstorm weather, strong wind weather, stormy weather, etc.

[0135] Secondly, a normal time consumption is obtained based on the fault type and the maintenance data; for example, under normal circumstances, a normal average time required to repair a damaged circuit breaker is calculated.

[0136] Finally, the third weather index is obtained based on the abnormal time consumption and the normal time consumption. For example, the third weather index is obtained by taking the difference between the two average times and calculating the ratio of the difference to the normal average time.

[0137] In this embodiment of the present application, the specific steps of obtaining the first self-healing strategy based on the second topology are as follows:

[0138] First, a key node is obtained based on the distribution network structure and the second key area, a key line is obtained based on the key node and the new fault location, and a first topology map is obtained based on the key line and the new fault signal data;

[0139] For example, based on the distribution network structure, new fault location and new fault signal data, the self-healing line with the largest scope is obtained, the equipment and lines corresponding to the key areas are obtained, and the key nodes and key connecting lines are obtained; in the self-healing line with the largest scope, all connecting lines and all nodes connected to the key nodes and key connecting lines are obtained in turn to obtain the first topology map.

[0140] Secondly, a comprehensive index is obtained based on the fault index and the weather index, such as by assigning different weights to obtain the comprehensive index, and the first topology map is screened based on the comprehensive index and the distribution network structure to obtain a second topology map;

[0141] For example, the nodes in the first topology graph are screened based on the comprehensive index of each node to obtain the second topology graph.

[0142] Finally, based on the second topology graph, the first self-healing strategy is obtained.

[0143] It should be noted that establishing a first self-healing strategy based on the first and second data and the target area's distribution network structure can achieve rapid response and efficient fault handling in actual distribution network operation. When a distribution network fault occurs, the strategy can be quickly activated and, based on real-time data and analysis results, automatically locate the fault point, assess the fault impact range, and generate an optimal fault isolation and recovery plan. This not only reduces the impact of the fault on the overall operation of the power grid, but also ensures that the power supply needs of key users or important facilities are met, improving the reliability and stability of the power grid. At the same time, the establishment of this strategy also takes into account multiple factors, such as historical fault data, distribution network data, geographic data, weather data, and target personnel data, making the strategy more comprehensive and scientific, and better able to adapt to the complex and changing distribution network environment. In addition, the strategy can be dynamically adjusted and optimized based on actual conditions to ensure that it always maintains optimal performance, providing strong guarantees for the safe and stable operation of the distribution network.

[0144] S104: Perform self-healing on the distribution network of the overhead line according to the first self-healing strategy.

[0145] In an optional embodiment, according to the first self-healing strategy, the system can automatically perform a series of preset operations to achieve self-healing of the distribution network. These operations include but are not limited to rapid positioning of the fault point, accurate assessment of the scope of the fault impact, isolation of the fault area, and restoration of power supply to non-fault areas. During the self-healing process, the system needs to monitor the status of the distribution network in real time and fine-tune the self-healing strategy according to actual conditions to ensure the efficiency and accuracy of the self-healing process. In addition, the system needs to record key data in the self-healing process, such as self-healing time, restored power supply, etc., to provide data support for subsequent strategy optimization. Through the implementation of the first self-healing strategy, the distribution network of the overhead line can quickly restore power supply in the event of a fault, reduce the time and scope of power outages, and improve the reliability and stability of the power grid.

[0146] In summary, the present invention proposes a distribution network protection and self-healing method based on overhead lines, which obtains first data and second data of the target area power grid; establishes a target area distribution network structure based on several data in the first data; establishes a first self-healing strategy based on the first data, the second data and the target area distribution network structure; and self-heals the distribution network of the overhead lines according to the first self-healing strategy. The present invention takes the target personnel as the core and gives priority to ensuring the power supply needs of the target personnel; comprehensively considers all possible fault situations by calculating fault indicators from factors such as geography, weather, failure rate and time; adds weather factors to reduce the problem of extended maintenance time due to weather factors, which leads to extended fault time. It does not focus on a single consideration factor, but obtains a self-healing strategy through multi-dimensional analysis, which is more complete.

[0147] Example 2: In a preferred embodiment, the target area is further divided into several sub-areas, and the regional fault location of each sub-area is obtained; the fault characteristics of the regional fault location are extracted, and the fault characteristics are input into a pre-trained prediction model to predict the third failure rate of each node; the fault characteristics include the fault type, the faulty device, the service life of the faulty device, the material of the faulty device, the fault location type, the height of the faulty device, the fault season, and the operating parameters of the faulty device before and after the fault; the fault location types include intersections, busbars, construction points, wind outlet points, and tower points;

[0148] The intersection point is obtained based on a preset number and the geographical structure; for example, based on the geographical structure, a node with more than 5 connection lines is obtained to obtain the intersection point;

[0149] Based on the first failure rate, the second failure rate, the third failure rate, the recovery rate, the recovery complexity, and the recovery time, a latest failure index is obtained, and the failure index is updated to the latest failure index.

[0150] By using historical failure data, we can infer the failure rate of non-faulty equipment in the future from multiple dimensions and further improve the self-healing strategy.

[0151] In this embodiment, the pre-trained prediction model can be a model pre-trained using algorithms such as deep neural networks and machine learning.

[0152] In a preferred embodiment, the third weather index may further include:

[0153] acquiring real-time weather data, and obtaining a stop time based on the real-time weather data;

[0154] Based on the stop time, a regional image of the target area is obtained, and based on the regional image, it is determined whether there is an abnormal point. If so, the abnormal position of the abnormal point is obtained, and the abnormal area is obtained based on the abnormal position, and the abnormal device in the abnormal area is obtained; based on the abnormal device, the node in the first topological map is deleted to obtain a third topological map; and the second topological map is updated to the third topological map.

[0155] The specific steps of determining whether there are abnormal points based on the regional image may include:

[0156] Based on the pre-trained recognition model, the regional image is identified to determine whether a waterlogged area, a fallen tree area, a hanging tree area, or a tilted tower area is identified. If so, an abnormal point is obtained.

[0157] In this embodiment, the pre-trained recognition model may be a model pre-trained using techniques such as image recognition.

[0158] Stop time refers to the time when a weather phenomenon ends, such as the time when a rainstorm stops.

[0159] The tree hanging area refers to the area where branches hang on the line.

[0160] Considering that after a storm or other weather conditions, some areas may experience waterlogging. Before the problem is properly resolved, to reduce further damage caused by the failure, the area is not suitable for power restoration. Therefore, the equipment in the area is removed from the self-healing strategy.

[0161] In a preferred embodiment, the real-time coordinates and real-time time of the maintenance personnel can also be obtained to determine whether the real-time time is within a preset time range. If not, the predicted time is obtained based on the real-time coordinates and the new fault point, and the latest time index is obtained based on the predicted time, and the time index is updated to the latest time index.

[0162] If the current time is 3 p.m., which is not within the range of 6-7 p.m., the remaining time is predicted by calculating the distance between the current maintenance personnel and the new fault point. The time index corresponding to the remaining time is obtained based on the set time range level. For example, if the remaining time is 35 minutes and the corresponding time range is 30-45 minutes, the time index is 0.8.

[0163] In a preferred embodiment, a circuit library may be constructed, the circuit library including a number of abnormal circuits, such as short circuits and abnormal connection circuits; abnormal device composition and abnormal connection relationship of each abnormal circuit may be obtained;

[0164] Obtain a self-healing circuit for the first self-healing strategy, split the self-healing circuit based on a preset device to obtain a plurality of sub-self-healing circuits, and obtain the sub-device composition and sub-connection relationship of each sub-self-healing circuit; for example, split the self-healing circuit by a circuit breaker.

[0165] Obtaining a first similarity between the sub-device composition and the abnormal device composition, obtaining a second similarity between the abnormal link relationship and the sub-connection relationship, and determining whether the sub-self-healing line is abnormal based on the first similarity and the second similarity. If so, obtaining an erroneous device and an erroneous connection relationship based on the sub-device composition and the sub-connection relationship;

[0166] The erroneous device and the erroneous connection relationship in the first topology map are deleted to obtain the latest first topology map, and the first topology map is updated to the latest first topology map.

[0167] For example, if the sub-device composition and the abnormal device composition each have 5 devices, and only one device is different, the first similarity is 4 / 5; the number of connections in the sub-connection relationship and the abnormal connection relationship are 8 and 10 respectively, and 6 of the connection relationships are different, then the second similarity is 6 / (8+10); different weights are assigned to the first similarity and the second similarity, and the final similarity is calculated. If its value is greater than the preset threshold, it is determined that the different device is an erroneous device, and the node and the edge connected to it are deleted in the first topology graph, and the self-healing strategy is re-obtained based on the comprehensive indicators.

[0168] Embodiment 3: This embodiment also provides a distribution network protection and self-healing system based on overhead lines, including:

[0169] A data acquisition module, configured to acquire first data and second data of a target area power grid;

[0170] A structure establishing module, configured to establish a target area distribution network structure according to some data in the first data;

[0171] A strategy establishment module, configured to establish a first self-healing strategy based on the first data, the second data, and the target area distribution network structure;

[0172] The first self-healing strategy includes:

[0173] Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data;

[0174] Acquire a first topology map according to the target area distribution network structure and the second data;

[0175] Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map;

[0176] Establishing a first self-healing strategy according to the second topology map;

[0177] The self-healing module is configured to perform self-healing on the distribution network of the overhead line according to the first self-healing strategy.

[0178] The above-mentioned unit modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0179] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a distribution network protection and self-healing method based on overhead lines is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0180] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0181] Acquiring first data and second data of a target area power grid;

[0182] Establishing a target area distribution network structure according to some data in the first data;

[0183] Establishing a first self-healing strategy based on the first data, the second data, and the target area distribution network structure;

[0184] The first self-healing strategy includes:

[0185] Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data;

[0186] Acquire a first topology map according to the target area distribution network structure and the second data;

[0187] Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map;

[0188] Establishing a first self-healing strategy according to the second topology map;

[0189] The distribution network of the overhead line is self-healed according to the first self-healing strategy.

[0190] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0191] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0192] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0195] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0196] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A distribution network protection and self-healing method based on overhead lines, characterized in that: include: Acquiring first data and second data of a target area power grid; Establishing a target area distribution network structure according to some data in the first data; Establishing a first self-healing strategy based on the first data, the second data, and the target area distribution network structure; The first self-healing strategy includes: Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data; Acquire a first topology map according to the target area distribution network structure and the second data; Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map; Establishing a first self-healing strategy according to the second topology map; The distribution network of the overhead line is self-healed according to the first self-healing strategy.

2. A distribution network protection and self-healing method based on overhead lines according to claim 1, characterized in that: Acquiring a first fault indicator according to the first data includes: The first failure indicator includes a first failure rate, a second failure rate, a recovery rate, a recovery complexity, and a recovery time; Acquire a fault event according to the first data, and acquire a fault order of the corresponding fault event in combination with the target area distribution network structure; A corresponding first failure rate is obtained according to the failure order.

3. A distribution network protection and self-healing method based on overhead lines according to claim 2, characterized in that: The obtaining of a first fault indicator according to the first data further includes: Acquire fault data of the fault point according to the first data, wherein the fault data includes fault frequency, fault type, fault location, maintenance data, equipment data, and fault range; obtaining a first fault index based on the fault frequency and the fault location; Obtaining a service life of the equipment based on the equipment data, and obtaining a second fault index based on the service life of the equipment; The second failure rate is obtained based on the first failure index and the second failure index.

4. A distribution network protection and self-healing method based on overhead lines according to claim 3, characterized in that: The obtaining of a first fault indicator according to the first data further includes: Obtaining the recovery rate based on the fault type and the maintenance data; Obtaining the restoration complexity based on the maintenance data and the fault scope; The restoration time is obtained based on the maintenance data and the maintenance preparation time.

5. A distribution network protection and self-healing method based on overhead lines according to claim 4, characterized in that: The first data and the second data include: The first data includes historical fault data, distribution network data, geographic data, weather data and first target personnel data; The second data includes new fault location and new fault signal data.

6. A distribution network protection and self-healing method based on overhead lines according to claim 5, characterized in that: The third weather index includes: Obtaining a weather type and duration based on the weather data, and obtaining abnormal time consumption based on the fault type, the maintenance data, the weather type and the duration; Obtaining a normal time consumption based on the fault type and the maintenance data; The third weather index is obtained based on the abnormal time consumption and the normal time consumption.

7. A distribution network protection and self-healing method based on overhead lines according to claim 6, characterized in that: The first self-healing strategy further includes: Obtaining a key node based on the target area distribution network structure and the second key area; Obtaining a key line based on the key node and the new fault location; Obtaining a first topology map based on the key line and the new fault signal data; Obtaining a comprehensive index based on the first fault index and the third weather index; Based on the comprehensive indicator and the distribution network structure, the first topology map is screened to obtain a second topology map; Based on the second topology graph, the first self-healing strategy is obtained.

8. A distribution network protection and self-healing system based on overhead lines, applying the method according to any one of claims 1 to 7, characterized in that: include: A data acquisition module, configured to acquire first data and second data of a target area power grid; a structure establishing module, configured to establish a target area distribution network structure according to a plurality of data in the first data; A strategy establishment module, configured to establish a first self-healing strategy based on the first data, the second data, and the target area distribution network structure; The first self-healing strategy includes: Acquire a first fault indicator, a second key area, and a third weather index according to the target area distribution network structure and the first data; Acquire a first topology map according to the target area distribution network structure and the second data; Filtering the first topology map based on the first fault indicator, the second key area, and the third weather index to obtain a second topology map; Establishing a first self-healing strategy according to the second topology map; The self-healing module is configured to perform self-healing on the distribution network of the overhead line according to the first self-healing strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a distribution network protection and self-healing method based on overhead lines according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a distribution network protection and self-healing method based on overhead lines according to any one of claims 1 to 7 are implemented.

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