A local sub-control method and platform for a network blocking operation integrated machine

Abstract

The present invention discloses a local sub-control method and platform of a network sealing operation integrated machine, which relates to the field of smart grid technology, including: obtaining the transmission line structure of the network sealing operation integrated machine; performing safety network analysis according to the transmission line structure, and generating a network sealing topology structure; establishing a regional division model, identifying node connectivity of the network sealing topology structure, and outputting N divided areas; obtaining N network sealing operation data sets of N divided areas, including network sealing operation equipment data and network sealing operation sensor data; by performing operation abnormality identification on the N network sealing operation data sets, obtaining abnormal isolation instructions, and performing line laying isolation control on the abnormal divided area according to the abnormal isolation instructions. The present invention solves the technical problem of low control accuracy in the prior art when performing network sealing operations, realizes isolation control of abnormal areas, and achieves the technical effect of improving power grid security and operational stability.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a local sub-control method and platform for an integrated network sealing operation machine. Background Art

[0002] With the development of power systems and the promotion of smart grid technology, the safety, stability and operation efficiency of power grids are becoming increasingly important. In the daily maintenance and emergency response of power systems, grid closure is a key task. Grid closure refers to the formation of a safe isolation area by controlling the operating status of power lines to prevent the spread of faults or provide safety guarantees for specific maintenance operations. Traditional grid closure operations mainly rely on manual operations and a single control method, which have problems such as low control accuracy and are difficult to meet the needs of modern power grids for efficient, intelligent and safe management. Summary of the invention

[0003] The present application provides a local sub-control method and platform for an integrated network blocking operation machine, which is used to solve the technical problem of low control accuracy in the prior art when performing network blocking operations.

[0004] In view of the above problems, the present application provides a local sub-control method and platform for a network blocking operation all-in-one machine.

[0005] In a first aspect of the present application, a local area sub-control method of a network blocking operation integrated machine is provided, the method comprising:

[0006] Acquire the transmission line structure of the network sealing operation integrated machine; perform safety net analysis based on the transmission line structure to generate a network sealing topology structure; establish a regional division model, identify the node connectivity of the network sealing topology structure based on the regional division model, and output N divided areas, wherein the node connectivity index within each divided area is greater than a preset threshold, and the node connectivity between each divided area and the adjacent divided area is less than the preset threshold; obtain N network sealing operation data sets of the N divided areas, including network sealing operation equipment data and network sealing operation sensor data; obtain abnormal isolation instructions by performing operation abnormality identification on the N network sealing operation data sets, and perform line laying isolation control on the abnormal divided areas according to the abnormal isolation instructions.

[0007] The second aspect of the present application provides a local sub-control platform of a network blocking operation integrated machine, the platform comprising:

[0008] A line structure acquisition module, wherein the line structure acquisition module acquires the structure of the transmission line on which the network sealing operation integrated machine acts; a safety network analysis module, wherein the safety network analysis module performs safety network analysis based on the transmission line structure and generates a network sealing topology structure; a connectivity identification module, wherein the connectivity identification module establishes a regional division model, performs node connectivity identification on the network sealing topology structure based on the regional division model, and outputs N divided areas, wherein the node connectivity index within each divided area is greater than a preset threshold, and the node connectivity between each divided area and the adjacent divided area is less than the preset threshold; a network sealing operation data acquisition module, wherein the network sealing operation data acquisition module acquires N network sealing operation data sets of the N divided areas, including network sealing operation equipment data and network sealing operation sensor data; an operation abnormality identification module, wherein the operation abnormality identification module obtains abnormal isolation instructions by performing operation abnormality identification on the N network sealing operation data sets, and performs line laying isolation control on the abnormal divided areas according to the abnormal isolation instructions.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application obtains the transmission line structure of the network sealing operation integrated machine; performs safety network analysis according to the transmission line structure, and generates a network sealing topology structure; establishes a regional division model, identifies node connectivity of the network sealing topology structure according to the regional division model, and outputs N divided areas, wherein the node connectivity index in each divided area is greater than a preset threshold, and the node connectivity between each divided area and the adjacent divided area is less than the preset threshold; obtains N network sealing operation data sets of N divided areas, including network sealing operation equipment data and network sealing operation sensor data; obtains abnormal isolation instructions by performing operation anomaly identification on N network sealing operation data sets, and performs line laying isolation control on abnormal divided areas according to the abnormal isolation instructions. The present invention solves the technical problem of low control accuracy in the prior art when performing network sealing operations. By constructing a regional division model, analyzing the transmission line structure, and using node connectivity identification technology, a network sealing topology structure is generated and N areas are divided. In combination with the network sealing operation data set and abnormal identification, isolation control of abnormal areas is achieved, achieving the technical effect of improving power grid security and operational stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in 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 creative work.

[0012] Figure 1A schematic flow chart of a local area sub-control method of a network blocking operation integrated machine provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a local sub-control platform of a network sealing operation integrated machine provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: line structure acquisition unit 11, safety network analysis unit 12, connectivity identification unit 13, network blocking operation data acquisition unit 14, operation abnormality identification unit 15. DETAILED DESCRIPTION

[0015] The present application provides a local sub-control method and platform for a network sealing operation integrated machine, aiming to solve the technical problem of low control accuracy in the prior art when performing network sealing operations. By constructing a regional division model, analyzing the transmission line structure, and using node connectivity identification technology, a network sealing topology structure is generated and divided into N areas. Combined with the network sealing operation data set and anomaly identification, isolation control of abnormal areas is achieved, thereby achieving the technical effect of improving power grid security and operational stability.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, platform, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a local sub-control method of a network blocking operation integrated machine, the method comprising:

[0019] Step S100: obtaining the structure of the power transmission line on which the network sealing machine operates.

[0020] In the embodiment of the present application, by connecting to the database of the power company or related institutions, according to the preset query conditions, basic information related to the target transmission line is retrieved, including design drawings, construction records, and geographic location data, etc. Through this process, the transmission line structure of the network sealing operation integrated machine is obtained.

[0021] Step S200: Perform safety network analysis based on the transmission line structure to generate a closed network topology.

[0022] Furthermore, in the method provided in the embodiment of the application, a safety network analysis is performed according to the transmission line structure to generate a closed network topology structure, and the method further includes:

[0023] Performing a grid area importance and grid area risk level analysis on the transmission line structure, outputting an importance factor and a risk level factor; inputting the importance factor and the risk level factor into a safety net analysis module, defining a safety net range for the transmission line structure, and outputting an initialized closed network topology structure; performing a safety verification on the initialized closed network topology structure, and generating a closed network topology structure.

[0024] In the embodiment of the present application, when the safety net analysis is performed according to the transmission line structure, the importance of the power grid area and the risk level of the power grid area are first analyzed for the transmission line structure. Specifically, when performing the importance analysis of the power grid area, the topological structure of the transmission line is analyzed to identify the key nodes and areas in the power grid, including substations, important intersections, and line sections supporting the main loads. After identifying the key nodes and areas, the importance of these nodes and areas is sorted and scored using the hierarchical analysis method. In the process of sorting and scoring, a hierarchical model is first constructed. This model includes the top-level goal, that is, determining the importance of each node and area; the middle-level criteria, such as load capacity, fault recovery time, and power supply range; and the bottom-level specific nodes and areas, such as substations, main transmission lines, and intersections. Then, the importance of the nodes and areas under each criterion of the middle layer is evaluated by the method of paired comparison. For example, under the criterion of power supply range, the relative importance of two nodes is compared in pairs, and a comparison value is assigned, such as node A is 3 times more important than node B, or the importance of A is 1 / 3 of B. The comparison values ​​of all nodes and regions are constructed into a pairwise comparison matrix. Then the pairwise comparison matrix is ​​standardized, and the average value of each row is calculated to obtain the weight of each node and region, which reflects their importance under a specific criterion. Next, the rationality of the pairwise comparison matrix is ​​ensured by consistency test, and the consistency index (CI) and consistency ratio (CR) are calculated. If the CR is less than 0.1, it means that the consistency of the matrix is ​​acceptable. Finally, the weights of all criterion layers are combined to calculate the comprehensive weight of each node and region. These comprehensive weights are their importance factors.

[0025] When conducting a regional risk level analysis for a power grid, we first identify the various types of faults that may affect the transmission lines, including natural disasters, equipment failures, and human factors. Next, we extract the frequency of each type of fault in each region from historical data. For example, in the past 10 years, one region has experienced multiple line damages due to storms, while another region may be mainly facing equipment aging problems. These data are counted to calculate the historical probability of each type of fault. Then, using the Monte Carlo simulation method, we generate multiple random scenarios to simulate the operation of the line under various fault conditions. Each scenario randomly introduces fault events and evaluates their impact on line operation. After a large number of simulation runs, the frequency of faults in each region under all simulated scenarios is counted. Next, we construct a risk matrix with fault probability and fault impact severity as the two dimensions of the matrix. Impact severity is determined by analyzing the potential consequences of the fault on the power grid, taking into account factors such as the scope of the power outage, recovery time, equipment damage, and economic losses. Assess the severity of the fault impact in each region. For example, a failure at a substation may cause a large-scale power outage and a long recovery time, so its impact level may be assessed as "catastrophic." Then, based on the probability of failure and the severity of the impact, find the corresponding cell in the risk matrix. For example, if a failure is assessed as "medium" probability and "severe" impact, the cell may be located at the intersection of the "medium probability" row and the "severe impact" column, with a score of 15. Combine the impact score obtained through the risk matrix with the probability of failure to calculate a preliminary risk score for each area. For example, if the probability of failure in an area is 5% and the impact score is 20, its preliminary risk score is 1. The preliminary risk scores are then standardized to facilitate comparison between different areas. Finally, the standardized risk score is used as the risk level factor for the area.

[0026] Through the above process, the importance factor and risk level factor are obtained.

[0027] Next, the importance factor and risk level factor of each region are input into the safety net analysis module. After input into the module, the importance factor and risk level factor are normalized by the minimum-maximum normalization method to ensure that they are comprehensively evaluated on the same scale. Then, weights are assigned to the importance factor and risk level factor according to the power grid security strategy and specific needs. Assume that the weight distribution is 70% importance factor and 30% risk level factor, that is, when defining the scope of the safety net, more emphasis is placed on the importance of the region, but the risk of the region is also fully considered. Next, the normalized factor of each region is weighted. Specifically, the importance factor of each region is multiplied by its weight, and the risk level factor is multiplied by its weight, and then the two are added to obtain the comprehensive score of each region. Then the comprehensive scores of all regions are sorted. The higher the score, the more important and risky the region is in the power grid, so it is preferentially included in the scope of the safety net. According to the sorting results, determine which areas should be protected. According to the sorting results, define the scope of the safety net. Specifically, key protection areas are delineated for high-scoring areas, and these areas will be included in the coverage of the safety net in the initialization of the closed network topology. After the safety net scope is defined, the initialization sealing net topology is generated.

[0028] Finally, the generated initialization network sealing topology is verified for security. In this step, the fault tree analysis method is used to test the effectiveness of the network sealing topology by simulating various possible failure scenarios. For example, a key node failure is simulated to verify whether the network sealing structure can effectively isolate the failure and prevent it from spreading. If the security verification is passed, the network sealing topology is generated.

[0029] Step S300: Establish a regional partition model, identify node connectivity of the closed network topology structure according to the regional partition model, and output N partitioned areas, wherein the node connectivity index within each partitioned area is greater than a preset threshold, and the node connectivity between each partitioned area and the adjacent partitioned area is less than the preset threshold.

[0030] In the embodiment of the present application, in the process of establishing the regional division model and identifying the node connectivity, the topological structure data of the power grid is first collected, including the location of the nodes, the connection relationship, and the transmission capacity and other information. Based on these data, a graph theory algorithm, such as a clustering algorithm or a community detection algorithm, is used to build a regional division model, and the nodes in the power grid are divided into several regions according to the strength of connectivity, so that the connectivity between nodes in the same region is strong, while the connectivity between different regions is relatively weak.

[0031] After completing the connectivity analysis, the regions are divided according to the preset threshold. The preset threshold is a connectivity standard that is used to determine the minimum connectivity requirement for nodes to be classified into the same region. Based on this threshold, nodes with connectivity indicators greater than the threshold are divided into the same region, and the connectivity of nodes within each divided region is ensured to meet the requirements.

[0032] At the same time, the node connectivity between adjacent partitioned areas is analyzed. The goal here is to ensure that the node connectivity between each partitioned area is lower than the preset threshold to avoid the risk diffusion caused by excessive connectivity between areas during failure. By comparing and adjusting the connectivity of adjacent areas, N partitioned areas are finally output, and the node connectivity index in each area is greater than the preset threshold, while the node connectivity between areas is less than the preset threshold.

[0033] Furthermore, in the method provided in the embodiment of the application, node connectivity is identified for the closed network topology structure according to the area division model, and N divided areas are output, and the method further includes:

[0034] According to the area partitioning model, node connectivity of the closed network topology structure is identified to obtain an initialization partition area set; internal connectivity of each area in the initialization partition area set is calculated to obtain an internal connectivity set; inter-domain connectivity of each pair of adjacent sub-areas in the initialization partition area set is calculated to obtain an inter-domain connectivity set; based on a boundary identification algorithm, the boundary of the initialization partition area set is adjusted using the internal connectivity set and the inter-domain connectivity set, and N partition areas are output.

[0035] In an embodiment of the present application, when the node connectivity of the closed network topology is identified according to the regional partitioning model, a connectivity matrix is ​​first constructed, which is an n×n matrix, where n represents the number of nodes in the power grid. Each element in the matrix represents the connectivity strength between two nodes, which can be the number of paths between the nodes, the path length, or the transmission capacity. For example, if there are multiple paths connecting two nodes and the transmission capacity is large, the value of the connectivity strength will be relatively high. Then, graph theory algorithms such as breadth-first search (BFS) or depth-first search (DFS) are used to analyze the connectivity of each node in the power grid based on the connectivity matrix. Highly connected node clusters are identified by these algorithms, and these node clusters are preliminarily divided into the same area. Based on the above analysis results, an initialization partition area set is generated, which contains several preliminarily divided areas, and the nodes in each area have strong connectivity.

[0036] Next, the internal connectivity of each area in the initialization partition area set is calculated using the network density formula. Network density refers to the ratio of the actual number of connections in the area to the maximum number of possible connections. When calculating, network density = (2 × actual number of connections) / ( ), where n is the number of nodes in the region. The higher the network density, the more tightly connected the nodes in the region are. At the same time, the clustering coefficient of each node is calculated to evaluate how tightly connected the nodes are in the local network. When calculating, the clustering coefficient is the number of actual connections between the neighbors of the node divided by the maximum possible number of connections between the neighbors of the node. Regions with high clustering coefficients indicate that their internal nodes are more tightly connected. The clustering coefficient is calculated for all nodes in each region, and the average value is taken as the clustering coefficient of the region. Finally, the network density and clustering coefficient are combined to generate the internal connectivity set of each region, which represents the connectivity strength in each region.

[0037] Then, the inter-domain connectivity of each pair of adjacent sub-regions in the set of initialized partitioned regions is calculated. Specifically, the key factors affecting the connectivity between adjacent sub-regions are first identified, including transmission capacity, connection frequency, and the number of connection paths. The transmission capacity refers to the power or data transmission capacity of the line connecting the two regions. The larger the transmission capacity, the stronger the connectivity between the regions. The connection frequency is the frequency of communication or data transmission between the two regions. The higher the frequency, the stronger the connectivity between the regions. The number of connection paths is the number of paths connecting the two regions. More paths mean higher redundancy and stronger connectivity. Based on the importance of the above factors, the system assigns an initial weight to each factor. For example, the transmission capacity accounts for 50% of the weight because it has the greatest direct impact on power and data transmission. The connection frequency accounts for 30% of the weight because it reflects the interaction intensity between regions. The number of connection paths accounts for 20% of the weight because it represents the redundancy and diversity between regions. In order to compare different factors on the same scale, the data of each factor is standardized. The purpose of standardization is to convert the data of each factor to a range between 0 and 1. When standardizing, the standardized value = (current value-minimum value) / (maximum value-minimum value). After completing data normalization, the comprehensive connectivity weight of each pair of adjacent sub-regions is calculated according to the initial weight of each factor. For example, consider a pair of adjacent regions A and B. Assuming that their standardized transmission capacity value is 0.8, the connection frequency value is 0.6, and the number of connection paths is 0.7, then their comprehensive connectivity weight = 0.8×0.5+0.6×0.3+0.7×0.2=0.72. This comprehensive weight represents the overall connectivity strength between regions A and B. The inter-domain connectivity of each pair of adjacent sub-regions is calculated, and an inter-domain connectivity set is formed, which contains the connectivity scores between all adjacent regions.

[0038] Finally, the boundary recognition algorithm is used to adjust the boundaries of the initial partitioned region set based on the internal connectivity set and the inter-domain connectivity set. The boundary recognition algorithm determines whether some boundaries need to be adjusted by comparing the connectivity differences inside and outside the region. The minimum cut algorithm is applied to determine which boundaries should be adjusted. The minimum cut algorithm identifies the edges with the greatest impact in cross-region connections (i.e., "cut edges") and optimizes the distribution of these edges to weaken the connectivity between regions and enhance the connectivity within the region. According to the results of the minimum cut algorithm, the region boundaries are adjusted and some nodes are reallocated to new regions. The optimized region division is more in line with the preset connectivity standards. Through the above process, the boundaries of the initial partitioned region set are adjusted and N partitioned regions are output.

[0039] Furthermore, in the method provided in the embodiment of the application, the boundary of the set of initialized partitioned areas is adjusted based on the boundary recognition algorithm using the internal connectivity set and the inter-domain connectivity set, and further includes:

[0040] If the internal connectivity corresponding to the current initialization partition area is less than or equal to the preset threshold, obtain the adjacent initialization partition area; calculate the centrality index of each node in the adjacent initialization partition area, and the centrality index is obtained by calculating the degree centrality and betweenness centrality of the node; preferentially select nodes with small centrality index from the adjacent initialization partition area, and adjust them to the current initialization partition area until the internal connectivity corresponding to the current initialization partition area is greater than the preset threshold.

[0041] In an embodiment of the present application, when the corresponding internal connectivity of the current initialization partition area is less than or equal to a preset threshold, first identify other initialization partition areas directly adjacent to the current initialization partition area to obtain adjacent initialization partition areas. Next, calculate the centrality index for each node in the adjacent area. The centrality index is obtained by calculating the degree centrality and betweenness centrality of the node. Degree centrality refers to the number of direct connections between a node and other nodes. The higher the degree centrality, the more important the node is in the network. For a node The degree centrality of , its calculation formula ,in Is a node The degree of the node The number of other nodes directly connected. Then iterate through the nodes in each adjacent region and calculate their degree centrality. For example, if a node is directly connected to 5 other nodes, then the degree centrality of the node is 5.

[0042] Betweenness centrality measures the importance of a node as a bridge in the network, specifically the number of shortest paths passing through the node. Betweenness centrality The calculation formula ,in is the number of all shortest paths from node s to node t, These are the shortest paths passing through the nodes This formula means that the node The proportion of the shortest paths between all other node pairs. Traverse the nodes in each neighboring region and calculate their betweenness centrality. To calculate the betweenness centrality of a node, first calculate all the shortest paths between each pair of nodes, and then count how many shortest paths pass through the node.

[0043] In order to compare degree centrality and betweenness centrality on the same scale, these two indicators are standardized. After standardization, the value of each centrality indicator will be normalized to the range of 0 to 1. Finally, the centrality indicator of each node is calculated. The centrality indicator is the weighted sum of degree centrality and betweenness centrality, and the calculation formula is ,in and is the preset weight parameter, which is usually set equal, that is, = =0.5.

[0044] Based on the calculated centrality index, nodes with lower centrality index are preferentially selected from adjacent regions. Since these nodes play a smaller role in the original region, moving them has less impact on the connectivity of the original region. After selecting these nodes, they are adjusted from adjacent regions to the current region with insufficient internal connectivity. By gradually adjusting nodes with lower centrality index to the current region, the internal connectivity of the region is gradually improved. After each adjustment, the connectivity index of the current region is re-evaluated until the internal connectivity corresponding to the current initialized partition region is greater than the preset threshold.

[0045] Furthermore, in the method provided in the embodiment of the application, the boundary of the set of initialized partitioned areas is adjusted based on the boundary recognition algorithm using the internal connectivity set and the inter-domain connectivity set, and further includes:

[0046] When adjusting a node to the current initialized partition area, recalculate the inter-domain connectivity between the current initialized partition area and the adjacent initialized partition area; if the recalculated inter-domain connectivity is greater than or equal to the preset threshold, optimize the adjusted node until the inter-domain connectivity between the current initialized partition area and the adjacent initialized partition area is less than the preset threshold, and so on, output N partition areas.

[0047] In the embodiment of the present application, after one or more nodes are adjusted from the adjacent region to the current region, the inter-domain connectivity between the current initialization partition region and the adjacent initialization partition region is first recalculated. The inter-domain connectivity represents the connection strength between two adjacent regions, and is calculated by the same method as described above. When calculating, the connection matrix is ​​regenerated according to the connection status of the adjusted nodes, and the connectivity between these adjusted nodes and the nodes in the adjacent region is calculated.

[0048] After recalculating the connectivity between the current region and the adjacent region, the new connectivity value is compared with the preset threshold. If the new connectivity value is less than the threshold, it means that the connection strength between the two regions is within the safe range and will not lead to excessive inter-regional dependence. If the recalculated inter-domain connectivity is greater than or equal to the preset threshold, it indicates that the connectivity between the current region and the adjacent region is too strong, which may affect the safe operation of the power grid. In this case, the adjusted nodes are optimized. The goal of the optimization is to weaken the connection strength between the two regions to below the safety threshold. When optimizing, some of the adjusted nodes are moved back to the original region, or the connection mode of these nodes is further optimized. For example, reduce the cross-regional connection path or reduce the transmission capacity to reduce the inter-domain connectivity. By iteratively adjusting and optimizing the nodes, the inter-domain connectivity is gradually reduced until it is less than the preset threshold. In this process, each adjustment is accompanied by the recalculation and verification of the connectivity index to ensure the effectiveness of the adjustment and the rationality of the regional division.

[0049] After the inter-domain connectivity between the current region and the adjacent regions is optimized to less than the preset threshold, further adjustments are stopped and the current region division result is retained. This means that the internal connectivity of the current region has been improved, and the connectivity between regions has been effectively controlled, and finally N divided regions are output.

[0050] Step S400: Acquire N network blocking operation data sets of the N divided areas, including network blocking operation equipment data and network blocking operation sensor data.

[0051] In an embodiment of the present application, firstly, according to the N divided areas, the key data collection points in each area are determined. These collection points include equipment nodes used in the network sealing operation process, such as network sealers, circuit breakers, and sensor nodes installed in key positions, such as voltage sensors and current sensors. Next, the network sealing operation equipment data is collected. The equipment data includes information such as the real-time operating status, operating parameters and workload of the equipment, such as the on / off status of the network sealer, the number of operations of the circuit breaker, and the operating temperature of the equipment. Through the interface with the equipment control system, modern industrial control technologies such as SCADA systems are used to monitor and collect these equipment data in real time. At the same time, the sensor data of the network sealing operation is collected through the sensor network. These data come from various sensors in the area, including voltage sensors, current sensors, temperature sensors, etc., reflecting the power parameters and environmental conditions in the network sealing operation.

[0052] After completing the collection of equipment data and sensor data, these data are integrated into a network blocking operation data set for each area. Each data set contains the operating data of all key equipment in the area and the monitoring data of sensors.

[0053] Finally, N network blocking operation data sets are generated, each of which contains the equipment operation data and sensor monitoring data of the corresponding area.

[0054] Step S500: obtaining an abnormality isolation instruction by identifying an abnormality in the N network blocking operation data sets, and performing line laying isolation control on the abnormally divided area according to the abnormality isolation instruction.

[0055] In the embodiment of the present application, N network blocking operation data sets are first comprehensively analyzed, and these data sets include equipment operation data and sensor monitoring data in each area, such as voltage, current, temperature, equipment operation status, etc. Through real-time monitoring and analysis of these data, the current operation status of the network blocking operation is obtained.

[0056] Threshold-based anomaly detection methods are then applied to identify possible operational anomalies. Each key parameter has one or more preset normal operating ranges (thresholds). By continuously monitoring these parameters, when the real-time value of a parameter exceeds the preset threshold, the situation is identified as abnormal. For example, if the voltage suddenly exceeds the safe upper limit, or the current drops to an abnormally low level, these situations are immediately marked as abnormal events.

[0057] After detecting an operational anomaly, the anomaly is classified and the nature of the anomaly is identified, such as equipment failure, power fluctuations, or other anomalies. Based on the type of anomaly identified, a corresponding abnormal isolation instruction is generated. These instructions are used to guide isolation operations in the power grid to prevent the anomaly from further affecting other areas. For example, if an overload is detected on a device, an instruction is generated to shut down the power supply to the device or cut off part of the line, thereby reducing the overall pressure on the power grid.

[0058] After the abnormal isolation instruction is generated, the line laying isolation control is immediately executed. This process involves controlling switches, circuit breakers or other equipment in the power grid to isolate the area where the abnormality occurs from the rest of the power grid.

[0059] Furthermore, in the method provided in the embodiment of the application, the line laying isolation control is performed on the abnormal divided area according to the abnormal isolation instruction, and further includes:

[0060] Obtain the abnormal indicator source of the abnormal divided area. If the abnormal indicator source is an operation sensor abnormality, output the abnormal isolation instruction as a single area-abnormal isolation instruction. If the abnormal indicator source is an operation equipment abnormality, determine whether the abnormal value of the operation equipment abnormality is greater than or equal to a preset boundary value. If it is less than the preset boundary value, output the abnormal isolation instruction as a single area-abnormal isolation instruction; if it is greater than or equal to the preset boundary value, output the abnormal isolation instruction as an adjacent area-abnormal isolation instruction.

[0061] In an embodiment of the present application, when an abnormality is detected in a certain divided area, the specific source of the abnormality, that is, the abnormal indicator source, is first identified. The abnormal indicator source can be divided into two types: operation sensor abnormality and operation equipment abnormality. Operation sensor abnormality is caused by abnormal fluctuations in sensor data or beyond the normal range, such as abnormal readings of voltage, current or temperature sensors. Operation equipment abnormality refers to abnormal operating parameters of the equipment itself, such as excessive workload, overheating or mechanical failure of the equipment.

[0062] If the system identifies that the source of the abnormal indicator is an operational sensor abnormality, it indicates that the abnormality is limited to a single area where a specific sensor is located. Since sensor abnormalities usually do not directly lead to a chain reaction in adjacent areas, a single area-abnormal isolation instruction is generated. The purpose of this instruction is to isolate the abnormal area from the rest of the power grid to prevent the abnormality from spreading further, but it does not affect the normal operation of adjacent areas. For example, if the current sensor in a certain area shows an abnormality, shut down the relevant lines in the area for isolation.

[0063] If the abnormal indicator source is identified as an operating equipment abnormality, the severity of the equipment abnormality is further determined. To this end, the detected abnormal value is compared with a preset threshold value. If the abnormal value of the equipment abnormality is less than the preset threshold value, it means that the abnormal impact is small and limited to a single area. At this time, a single area-abnormal isolation instruction is generated to isolate only the abnormal area without affecting the operation of other areas. If the abnormal value of the equipment abnormality is greater than or equal to the preset threshold value, it means that the abnormality may have a greater impact and may even affect adjacent areas. In order to prevent larger-scale failures or accidents, an adjacent area-abnormal isolation instruction is generated to isolate not only the abnormal area, but also the adjacent areas.

[0064] Furthermore, the method provided in the application embodiment also includes:

[0065] When the abnormal isolation instruction is output as the adjacent area-abnormal isolation instruction, the updated network blocking control data of the abnormal divided area is obtained; the Nash equilibrium algorithm is adopted to perform Nash equilibrium adjustment on the adjacent area network blocking control data with the updated network blocking control data of the abnormal divided area, and the balanced network blocking control data is output; the line laying isolation control is performed with the updated network blocking control data and the balanced network blocking control data.

[0066] In the embodiment of the present application, when an abnormal area is detected and it is decided to implement neighboring area-abnormal isolation, updated network blocking control data is obtained from the abnormal area. These data include information such as the adjusted power load distribution, line status (such as disconnection or connection), voltage regulation, etc. of the area under the current abnormal state.

[0067] Next, it is necessary to ensure that the grid-locking control strategy between the abnormal area and its neighboring areas reaches a balanced state. For this purpose, the Nash equilibrium algorithm is applied. In this algorithm, each participant (i.e., different areas of the power grid) has its own grid-locking control strategy, and the goal is to maximize its own benefits by selecting the best strategy. Then a profit function is constructed for each area (including the abnormal area and the neighboring area). This profit function consists of multiple factors, mainly including load balancing benefits, operating cost benefits, and stability benefits. The profit function of each area is as follows:

[0068]

[0069] in is the strategy for region A, It is a combination of strategies from other regions. , and are the corresponding weights, which are pre-set by technical experts. is the functional form of load balancing benefit, is the functional form of the operating cost-effectiveness, is the functional form of the stability benefit.

[0070]

[0071] in, represents the actual load in area A, and They represent the maximum and minimum safe load ranges of area A respectively. is the optimal load value for region A. Maximum benefit at the time.

[0072]

[0073] in Indicates the operating cost of area A.

[0074]

[0075] in Represents the stability index of region A. Usually, the closer it is to 1, the more stable it is. When calculating the stability index, voltage stability, frequency stability, and load balance are considered. ,in Indicates the actual voltage in area A With nominal voltage The difference between the nominal voltage is the expected normal voltage value. The closer the value of this formula is to 1, the more stable the voltage is. Frequency stability ,in Indicates the actual frequency in area A With nominal frequency The difference between the nominal frequency It is a pre-set standard frequency, such as 50Hz or 60Hz. The higher the frequency stability, the smaller the frequency fluctuation of the system and the more stable the grid operation. ,in Indicates the actual load of area A The best load for the area The difference between the optimal load It refers to the most stable load level of the power grid in this area. The closer the load balance index is to 1, the more reasonable the load distribution is and the more stable the regional operation is. The weighted integration of the stability indexes of the above three parts gives the comprehensive stability index of area A. ,in , and are the corresponding weights, reflecting the importance of voltage, frequency and load balance in the overall stability, which are pre-set by technical experts.

[0076] The Nash equilibrium algorithm is used to calculate the best response strategy for each region, that is, the strategy that maximizes its own benefits when the strategies of other regions remain unchanged. The Nash equilibrium point is the best strategy combination of all regions. In this combination, no region can obtain higher benefits by unilaterally changing its strategy. The Nash equilibrium algorithm is used to generate balanced network blocking control data. These data reflect the network blocking control strategies that should be adopted by abnormal regions and adjacent regions under the Nash equilibrium state, including adjusted power load distribution, line status management, and voltage regulation.

[0077] Finally, the updated network blocking control data of the abnormal area is combined with the balanced network blocking control data obtained through the Nash equilibrium algorithm to perform line laying isolation control. Specific operations include adjusting and isolating related power lines, redistributing power loads, etc., to ensure that the abnormal area is effectively isolated, while the power supply and stability of the adjacent areas are also guaranteed.

[0078] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0079] The present application obtains the transmission line structure of the network sealing operation integrated machine; performs safety network analysis according to the transmission line structure, and generates a network sealing topology structure; establishes a regional division model, identifies node connectivity of the network sealing topology structure according to the regional division model, and outputs N divided areas, wherein the node connectivity index in each divided area is greater than a preset threshold, and the node connectivity between each divided area and the adjacent divided area is less than the preset threshold; obtains N network sealing operation data sets of N divided areas, including network sealing operation equipment data and network sealing operation sensor data; obtains abnormal isolation instructions by performing operation anomaly identification on N network sealing operation data sets, and performs line laying isolation control on abnormal divided areas according to the abnormal isolation instructions. The present invention solves the technical problem of low control accuracy in the prior art when performing network sealing operations. By constructing a regional division model, analyzing the transmission line structure, and using node connectivity identification technology, a network sealing topology structure is generated and N areas are divided. In combination with the network sealing operation data set and abnormal identification, isolation control of abnormal areas is achieved, achieving the technical effect of improving power grid security and operational stability.

[0080] Embodiment 2 is based on the same inventive concept as the local area sub-control method of the network blocking operation integrated machine in the above embodiment. Figure 2 As shown, the present application provides a local sub-control platform of a network blocking operation integrated machine, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. Among them, the platform includes:

[0081] A line structure acquisition unit 11, wherein the line structure acquisition unit 11 acquires the structure of the transmission line on which the network sealing operation integrated machine acts; a safety network analysis unit 12, wherein the safety network analysis unit 12 performs safety network analysis according to the transmission line structure and generates a network sealing topology structure; a connectivity identification unit 13, wherein the connectivity identification unit 13 establishes a region division model, performs node connectivity identification on the network sealing topology structure according to the region division model, and outputs N divided regions, wherein the node connectivity index within each divided region is greater than a preset threshold, and the node connectivity between each divided region and an adjacent divided region is less than the preset threshold; a network sealing operation data acquisition unit 14, wherein the network sealing operation data acquisition unit 14 acquires N network sealing operation data sets of the N divided regions, including network sealing operation equipment data and network sealing operation sensor data; an operation abnormality identification unit 15, wherein the operation abnormality identification unit 15 obtains abnormal isolation instructions by performing operation abnormality identification on the N network sealing operation data sets, and performs line laying isolation control on the abnormal divided regions according to the abnormal isolation instructions.

[0082] Furthermore, the platform is also used to implement the following functions:

[0083] According to the area partitioning model, node connectivity of the closed network topology structure is identified to obtain an initialization partition area set; internal connectivity of each area in the initialization partition area set is calculated to obtain an internal connectivity set; inter-domain connectivity of each pair of adjacent sub-areas in the initialization partition area set is calculated to obtain an inter-domain connectivity set; based on a boundary identification algorithm, the boundary of the initialization partition area set is adjusted using the internal connectivity set and the inter-domain connectivity set, and N partition areas are output.

[0084] Furthermore, the platform is also used to implement the following functions:

[0085] If the internal connectivity corresponding to the current initialization partition area is less than or equal to the preset threshold, obtain the adjacent initialization partition area; calculate the centrality index of each node in the adjacent initialization partition area, and the centrality index is obtained by calculating the degree centrality and betweenness centrality of the node; preferentially select nodes with small centrality index from the adjacent initialization partition area, and adjust them to the current initialization partition area until the internal connectivity corresponding to the current initialization partition area is greater than the preset threshold.

[0086] Furthermore, the platform is also used to implement the following functions:

[0087] When adjusting a node to the current initialized partition area, recalculate the inter-domain connectivity between the current initialized partition area and the adjacent initialized partition area; if the recalculated inter-domain connectivity is greater than or equal to the preset threshold, optimize the adjusted node until the inter-domain connectivity between the current initialized partition area and the adjacent initialized partition area is less than the preset threshold, and so on, output N partition areas.

[0088] Furthermore, the platform is also used to implement the following functions:

[0089] Obtain the abnormal indicator source of the abnormal divided area. If the abnormal indicator source is an operation sensor abnormality, output the abnormal isolation instruction as a single area-abnormal isolation instruction. If the abnormal indicator source is an operation equipment abnormality, determine whether the abnormal value of the operation equipment abnormality is greater than or equal to a preset boundary value. If it is less than the preset boundary value, output the abnormal isolation instruction as a single area-abnormal isolation instruction; if it is greater than or equal to the preset boundary value, output the abnormal isolation instruction as an adjacent area-abnormal isolation instruction.

[0090] Furthermore, the platform is also used to implement the following functions:

[0091] When the abnormal isolation instruction is output as the adjacent area-abnormal isolation instruction, the updated network blocking control data of the abnormal divided area is obtained; the Nash equilibrium algorithm is adopted to perform Nash equilibrium adjustment on the adjacent area network blocking control data with the updated network blocking control data of the abnormal divided area, and the balanced network blocking control data is output; the line laying isolation control is performed with the updated network blocking control data and the balanced network blocking control data.

[0092] Furthermore, the platform is also used to implement the following functions:

[0093] Performing a grid area importance and grid area risk level analysis on the transmission line structure, outputting an importance factor and a risk level factor; inputting the importance factor and the risk level factor into a safety net analysis module, defining a safety net range for the transmission line structure, and outputting an initialized closed network topology structure; performing a safety verification on the initialized closed network topology structure, and generating a closed network topology structure.

[0094] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0096] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A local sub-control method for a network sealing machine, characterized in that: The method comprises: Obtain the transmission line structure of the network sealing operation integrated machine; Perform safety network analysis based on the transmission line structure to generate a closed network topology structure; Establishing a regional partitioning model, identifying node connectivity of the closed network topology structure according to the regional partitioning model, and outputting N partitioning areas, wherein the node connectivity index within each partitioning area is greater than a preset threshold, and the node connectivity between each partitioning area and an adjacent partitioning area is less than the preset threshold; Acquire N network blocking operation data sets of the N divided areas, including network blocking operation equipment data and network blocking operation sensor data; By identifying the operation anomalies of the N network blocking operation data sets, an abnormal isolation instruction is obtained, and line laying isolation control is performed on the abnormal divided area according to the abnormal isolation instruction; The node connectivity of the closed network topology structure is identified according to the area division model, and N divided areas are output, including: Identify the node connectivity of the closed network topology structure according to the area division model to obtain an initialization division area set; Calculating the internal connectivity of each area in the initialization divided area set to obtain an internal connectivity set; Calculating the inter-domain connectivity of each pair of adjacent sub-regions in the set of initialized divided regions to obtain an inter-domain connectivity set; Based on a boundary recognition algorithm, adjusting the boundaries of the initialized partitioned area set with the internal connectivity set and the inter-domain connectivity set, and outputting N partitioned areas; The method further comprises: adjusting the boundaries of the initialization partition area set by using the internal connectivity set and the inter-domain connectivity set based on a boundary recognition algorithm, including: If the internal connectivity corresponding to the current initialization division area is less than or equal to the preset threshold, obtaining the adjacent initialization division area; Calculating the centrality index of each node in the adjacent initialization partition area, wherein the centrality index is obtained by calculating the degree centrality and betweenness centrality of the node; Prioritize nodes with smaller centrality index from the adjacent initialization partition areas, and adjust them to the current initialization partition area until the corresponding internal connectivity of the current initialization partition area is greater than the preset threshold; Based on a boundary recognition algorithm, the internal connectivity set and the inter-domain connectivity set are used to adjust the boundaries of the initialization partition area set, and further comprising: When adjusting the node to the current initialization partition area, recalculating the inter-domain connectivity between the current initialization partition area and the adjacent initialization partition area; If the recalculated inter-domain connectivity is greater than or equal to the preset threshold, the adjusted nodes are optimized until the inter-domain connectivity between the current initialized partition area and the adjacent initialized partition area is less than the preset threshold, and so on, outputting N partition areas; Wherein, performing line laying isolation control on the abnormally divided area according to the abnormal isolation instruction includes: Acquire the abnormality indicator source of the abnormal division area, and if the abnormality indicator source is an operation sensor abnormality, output the abnormality isolation instruction as a single area-abnormality isolation instruction; If the abnormal indicator source is an abnormal operation equipment, determine whether the abnormal value of the abnormal operation equipment is greater than or equal to a preset threshold value, if it is less than the preset threshold value, output the abnormal isolation instruction as a single area-abnormal isolation instruction, if it is greater than or equal to the preset threshold value, output the abnormal isolation instruction as an adjacent area-abnormal isolation instruction; When the abnormal isolation instruction is output as the adjacent area-abnormal isolation instruction, obtaining updated network blocking control data of the abnormal divided area; Adopting the Nash equilibrium algorithm, using the updated network blocking control data of the abnormal divided area to perform Nash equilibrium adjustment on the network blocking control data of the adjacent area, and outputting the balanced network blocking control data; Performing line laying isolation control using the updated network blocking control data and the balanced network blocking control data; Wherein, performing safety network analysis according to the transmission line structure to generate a closed network topology structure includes: Performing grid area importance and grid area risk level analysis on the transmission line structure, and outputting importance factors and risk level factors; Input the importance factor and the risk level factor into the safety network analysis module, define the safety network range of the transmission line structure, and output the initialization closed network topology structure; The initialization closed network topology structure is subjected to security verification to generate a closed network topology structure.

2. A local sub-control platform of a network sealing machine, characterized in that: The platform includes: A line structure acquisition unit, wherein the line structure acquisition unit acquires the structure of the power transmission line on which the network sealing operation integrated machine acts; A safety network analysis unit, wherein the safety network analysis unit performs safety network analysis according to the transmission line structure and generates a closed network topology structure; A connectivity identification unit, wherein the connectivity identification unit establishes a regional division model, identifies node connectivity of the closed network topology structure according to the regional division model, and outputs N divided regions, wherein the node connectivity index within each divided region is greater than a preset threshold, and the node connectivity between each divided region and an adjacent divided region is less than the preset threshold; A network blocking operation data acquisition unit, wherein the network blocking operation data acquisition unit acquires N network blocking operation data sets of the N divided areas, including network blocking operation equipment data and network blocking operation sensor data; an operation abnormality identification unit, wherein the operation abnormality identification unit identifies the operation abnormality of the N network blocking operation data sets, obtains an abnormal isolation instruction, and performs line laying isolation control on the abnormal divided area according to the abnormal isolation instruction; The node connectivity of the closed network topology structure is identified according to the area division model, and N divided areas are output, including: Identify the node connectivity of the closed network topology structure according to the area division model to obtain an initialization division area set; Calculating the internal connectivity of each area in the initialization divided area set to obtain an internal connectivity set; Calculating the inter-domain connectivity of each pair of adjacent sub-regions in the set of initialized divided regions to obtain an inter-domain connectivity set; Based on a boundary recognition algorithm, adjusting the boundaries of the initialized partitioned area set with the internal connectivity set and the inter-domain connectivity set, and outputting N partitioned areas; The method further comprises: adjusting the boundaries of the initialization partition area set by using the internal connectivity set and the inter-domain connectivity set based on a boundary recognition algorithm, including: If the internal connectivity corresponding to the current initialization division area is less than or equal to the preset threshold, obtaining the adjacent initialization division area; Calculating the centrality index of each node in the adjacent initialization partition area, wherein the centrality index is obtained by calculating the degree centrality and betweenness centrality of the node; Prioritize nodes with smaller centrality index from the adjacent initialization partition areas, and adjust them to the current initialization partition area until the corresponding internal connectivity of the current initialization partition area is greater than the preset threshold; Based on a boundary recognition algorithm, the internal connectivity set and the inter-domain connectivity set are used to adjust the boundaries of the initialization partition area set, and further comprising: When adjusting the node to the current initialization partition area, recalculating the inter-domain connectivity between the current initialization partition area and the adjacent initialization partition area; If the recalculated inter-domain connectivity is greater than or equal to the preset threshold, the adjusted nodes are optimized until the inter-domain connectivity between the current initialized partition area and the adjacent initialized partition area is less than the preset threshold, and so on, outputting N partition areas; Wherein, performing line laying isolation control on the abnormally divided area according to the abnormal isolation instruction includes: Acquire the abnormality indicator source of the abnormal division area, and if the abnormality indicator source is an operation sensor abnormality, output the abnormality isolation instruction as a single area-abnormality isolation instruction; If the abnormal indicator source is an abnormal operation equipment, determine whether the abnormal value of the abnormal operation equipment is greater than or equal to a preset threshold value, if it is less than the preset threshold value, output the abnormal isolation instruction as a single area-abnormal isolation instruction, if it is greater than or equal to the preset threshold value, output the abnormal isolation instruction as an adjacent area-abnormal isolation instruction; When the abnormal isolation instruction is output as the adjacent area-abnormal isolation instruction, obtaining updated network blocking control data of the abnormal divided area; Adopting the Nash equilibrium algorithm, using the updated network blocking control data of the abnormal divided area to perform Nash equilibrium adjustment on the network blocking control data of the adjacent area, and outputting the balanced network blocking control data; Performing line laying isolation control using the updated network blocking control data and the balanced network blocking control data; Wherein, performing safety network analysis according to the transmission line structure to generate a closed network topology structure includes: Performing grid area importance and grid area risk level analysis on the transmission line structure, and outputting importance factors and risk level factors; Input the importance factor and the risk level factor into the safety network analysis module, define the safety network range of the transmission line structure, and output the initialization closed network topology structure; The initialization closed network topology structure is subjected to security verification to generate a closed network topology structure.

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