New energy grid-connected path optimal configuration method and system based on knowledge graph technology

Abstract

The invention discloses a new energy grid-connected path optimal configuration method and system based on a knowledge graph technology, and relates to the technical field of power distribution network dispatching. The method comprises the following steps: obtaining power grid operation data, and carrying out scene similarity analysis and attribute similarity analysis on the power grid operation data to determine an abnormal map; expanding the capacity of the native database through the abnormal map, and extracting texture information and primitive information in the expanded target database to construct a power distribution network knowledge graph; and determining an abnormal symbiotic region according to the abnormal situation in combination with the power distribution network knowledge graph, obtaining a load transfer path according to an abnormal transition factor of the abnormal symbiotic region, and performing transfer situation analysis on the load transfer path to determine a target transfer strategy. According to the scheme, a multi-target path search algorithm driven by an abnormal transition factor is introduced to search an optimal transfer path meeting contact constraints, a transfer strategy considering reliability, economical efficiency and risk controllability is generated, and the generation efficiency and reliability of the transfer strategy are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of distribution network dispatching technology, and specifically, to a method and system for optimizing configuration of new energy grid-connected paths based on knowledge graph technology. Background Art

[0002] With the large-scale grid connection of new energy, the complexity and uncertainty of distribution networks have increased significantly. Traditional exception handling methods mainly rely on static databases and manual experience, and have the following technical barriers: 1) Relying on preset fault sets and static topology analysis, it is impossible to capture the correlation between new energy output fluctuations, dynamic load changes and abnormal evolution of the power grid in real time, resulting in delayed positioning of abnormal areas and fuzzy boundary characterization; 2) The semantic association of multi-source heterogeneous data (such as SCADA measurements, equipment ledgers, and historical operation and maintenance records) has not been established, resulting in a lack of global knowledge support for the prediction of abnormal propagation paths and the generation of transfer strategies; 3) The transfer path is only evaluated through topological connectivity or simple economic indicators, ignoring the impact of dynamic parameters such as equipment health status, real-time load rate, and risk on decision-making, which is prone to overload or failure of the transfer path in scenarios with high penetration of new energy.

[0003] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The purpose of the present invention is to address the problems of poor dynamic adaptability of abnormal scenarios, low prediction accuracy of abnormal propagation paths, and single load transfer strategies in the prior art. The present invention proposes a new energy grid-connected path optimization configuration method and system based on knowledge graph technology. Through dual analysis of scene similarity and attribute similarity, an abnormal map reflecting the spatiotemporal evolution law of power grid anomalies is dynamically constructed to achieve accurate characterization of abnormal boundaries; based on the causal chain and rule chain reasoning mechanism of the knowledge graph, the equipment texture information (such as electrical parameters) and the primitive information (such as topological connections) are deeply integrated to construct a dynamically expanded distribution network knowledge graph and enhance the correlation mining capability of abnormal symbiotic areas; a multi-objective path search algorithm driven by abnormal transition factors is introduced to search for the optimal transfer path that meets the connection constraints, and a transfer strategy that takes into account reliability, economy, and risk controllability is generated, which significantly improves the generation efficiency and reliability of the transfer strategy.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is: a new energy grid connection path optimization configuration method based on knowledge graph technology, comprising the following steps: Obtain power grid operation data and perform scene similarity analysis and attribute similarity analysis on it to determine the abnormal map; The native database is expanded through the abnormal map and the texture information and graphic element information in the expanded target database are extracted to build the distribution network knowledge graph; According to the abnormal situation and the knowledge graph of the distribution network, the abnormal symbiosis area is determined, and the load transfer path is obtained according to the abnormal transition factor of the abnormal symbiosis area. The load transfer situation is analyzed on the load transfer path to determine the target transfer strategy.

[0006] Preferably, obtaining power grid operation data and performing scene similarity analysis and attribute similarity analysis on the data to determine an abnormal map comprises the following steps: Obtain the abnormal records and corresponding repair records of the distribution network to determine the abnormal type, texture information and element information corresponding to each element in the time dimension; The abnormal scene of the distribution network is sliced ​​in the time domain based on the abnormal point to determine the abnormal portrait corresponding to each time domain slice; Determine the scene similarity of the abnormal image based on texture information and abnormal type; Determine the attribute similarity of the abnormal image based on the image element information and the abnormal type; The anomaly boundary and repair window of each time domain section are determined by scene similarity and attribute similarity, and an anomaly map is constructed based on the anomaly boundary and repair window.

[0007] As a preferred method, the construction process of the distribution network knowledge graph includes: Acquire the topological structure data and grid operation data of the distribution network during normal operation to build a native database; extract the time domain information matching the abnormal points in the native database, extract the texture information and graphic element information of the time domain section information corresponding to the abnormal point diagram, and determine the abnormal data based on the topological structure and flow direction with the abnormal point as the center; expand the native database according to the abnormal data to obtain the target database; extract the texture information in the target database to determine the causal chain and the graphic element information to determine the rule chain; determine the distribution network knowledge graph corresponding to the abnormal point according to the causal chain and the rule chain.

[0008] Preferably, the method of determining the abnormal coexistence area according to the abnormal situation in combination with the distribution network knowledge graph comprises the following steps: Extract the three-dimensional features of the current abnormal point, and index the historical abnormal records associated with the device nodes with the same name according to the distribution network knowledge graph; activate the time dimension label in the graph to match the time domain features corresponding to the current abnormal point; Taking the abnormal point as the center, calculate the Euclidean distance of all devices in the set area, and build the spatial proximity distance according to the topological structure; traverse the connection relationship and logical relationship in the knowledge graph to identify the abnormal propagation path; Construct a symbiotic feature library based on the homology and spatial proximity of devices; calculate the Jaccard coefficient of the current abnormal combination and the historical pattern in turn, and activate the initial symbiotic area range of the corresponding pattern based on the Jaccard coefficient; The state estimation factor of the symbiotic area is determined according to the abnormal situation and the health of the equipment, and the boundary of the initial symbiotic area is adjusted according to the state estimation factor to obtain the abnormal symbiotic area.

[0009] Preferably, the load transfer path is obtained according to the abnormal transition factor of the abnormal symbiotic area, and the load transfer situation analysis is performed on the load transfer path to determine the target transfer strategy; the steps include: The equipment health, historical transfer rate, and real-time load rate of the equipment in the abnormal symbiosis area are extracted from the distribution network knowledge graph and combined with the corresponding weight coefficient to determine the equipment abnormal transition factor; Taking the abnormal symbiotic area as the starting point, search for all device nodes that have contact with the area in the distribution network knowledge graph, and use the device nodes as candidate load transfer paths; A multi-dimensional transfer situation assessment index system is constructed by reliability index, economic index, timeliness index and risk index to calculate the comprehensive score of each candidate load transfer path, and the candidate load transfer path with the highest comprehensive score is taken as the target transfer path.

[0010] Preferably, starting from the abnormal symbiotic area, searching all device nodes that have contact with the area in the distribution network knowledge graph, and taking the device nodes as candidate load transfer paths; comprising the following steps: The constraint conditions of the path search are set, and the constraint conditions at least include the capacity limit of the tie switch and the voltage deviation threshold constraint; the improved Dijkstra algorithm is used to take the inverse of the abnormal transition factor as the heuristic information of the path search, and the shortest path to each potential transfer path node is gradually searched from the starting point; during the search process, whether the path meets the constraint conditions is continuously checked, and if not, the path is discarded; for the searched paths, the sum of the abnormal transition factors of all devices on each path is calculated, and the first n paths with the smallest sum of abnormal transition factors are selected as candidate load transfer paths.

[0011] As a preferred method, the historical power transfer records of the equipment on the path are obtained from the distribution network knowledge graph, and the ratio of the number of successful power transfers to the total number of power transfers is calculated to calculate the historical power transfer success rate to obtain the reliability index; Obtain the line resistance, transfer current and transfer time on the path from the distribution network knowledge graph to determine the transfer power loss and obtain the economic index; Obtain the switch action time of the equipment on the path, the operation time of the switch equipment, and calculate the total time required for the entire transfer process to obtain the timeliness index; The transfer risk value is calculated according to the sum of abnormal transition factors of equipment on the path, load fluctuation rate and their corresponding weights to obtain the risk index.

[0012] In a second aspect, a technical solution provided in an embodiment of the present invention is: a new energy grid-connected path optimization configuration system, comprising: Determination module: obtains power grid operation data and performs scene similarity analysis and attribute similarity analysis to determine the abnormal map; Construction module: Expand the native database through the abnormal map and extract the texture information and graphic element information in the expanded target database to build the distribution network knowledge graph; Generation module: Determine the abnormal symbiosis area based on the abnormal situation and the distribution network knowledge graph, obtain the load transfer path based on the abnormal transition factor of the abnormal symbiosis area, analyze the transfer situation of the load transfer path and determine the target transfer strategy.

[0013] In the third aspect, a technical solution provided in an embodiment of the present invention is: comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the new energy grid-connected path optimization configuration method based on knowledge graph technology are implemented.

[0014] In a fourth aspect, a technical solution provided in an embodiment of the present invention is: computer executable instructions are stored in the storage medium, and when the computer executable instructions are loaded and executed by the processor, the steps of the new energy grid-connected path optimization configuration method based on knowledge graph technology are implemented.

[0015] Beneficial effects of the present invention: (1) In order to solve the problem of lagging positioning and blurred boundaries in traditional abnormal area, this application dynamically constructs an abnormal map through dual analysis of scene similarity and attribute similarity, and realizes accurate depiction of abnormal boundaries. Specifically, the abnormal records and repair records are used for time domain slicing, and the scene similarity and attribute similarity are calculated by combining texture information (such as electrical parameters) and primitive information (such as topological connection). The abnormal map containing abnormal boundaries and repair windows is dynamically generated, which solves the problem that traditional static topological analysis cannot associate new energy fluctuations with dynamic changes in load in real time, significantly improves the positioning accuracy of abnormal areas, significantly shortens abnormal response time, and avoids the expansion of faults. (2) To address the problem of insufficient decision support due to the lack of semantic association of multi-source heterogeneous data, the causal chain (equipment electrical parameter association) and rule chain (topology connection logic) reasoning mechanisms are used to associate heterogeneous data such as SCADA measurements, equipment ledgers, and historical operation and maintenance records to form a dynamically scalable knowledge graph, enhance global knowledge reasoning capabilities, and support more complex power grid abnormality decision-making scenarios; (3) In order to solve the problem of single evaluation index of traditional power transfer path, this application introduces a multi-objective path search algorithm driven by abnormal transition factor to generate the optimal power transfer strategy taking into account multiple dimensions; abnormal transition factor is constructed by combining dynamic parameters such as equipment health, historical power transfer rate, and real-time load rate, and the improved Dijkstra algorithm is used to search for the optimal path that meets the connection constraints, and a comprehensive decision is made through a four-dimensional evaluation system of reliability, economy, timeliness, and risk. Avoid the risk of overload or failure of the power transfer path and significantly improve the resilience of the power grid.

[0016] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0018] Figure 1 This is a flow chart of the new energy grid-connected path optimization configuration method based on knowledge graph technology of the present invention.

[0019] Figure 2 It is a structural schematic diagram of the new energy grid-connected path optimization configuration system of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0022] Example: Figure 1 As shown in the figure, the new energy grid connection path optimization configuration method based on knowledge graph technology includes the following steps: S1. Obtain power grid operation data and perform scene similarity analysis and attribute similarity analysis to determine the abnormal map.

[0023] As an optional embodiment, S1 includes the following steps: Obtain the abnormal records and corresponding repair records of the distribution network to determine the abnormal type, texture information and element information corresponding to each element in the time dimension; The abnormal scene of the distribution network is sliced ​​in the time domain based on the abnormal point to determine the abnormal portrait corresponding to each time domain slice; Determine the scene similarity of the abnormal image based on texture information and abnormal type; Determine the attribute similarity of the abnormal image based on the image element information and the abnormal type; The anomaly boundary and repair window of each time domain section are determined by scene similarity and attribute similarity, and an anomaly map is constructed based on the anomaly boundary and repair window.

[0024] It is understandable that, taking a 10kV distribution network connected to a wind farm in a coastal area as an example, the region frequently experiences abnormal events such as voltage over-limit and line overload due to frequent fluctuations in wind power output. Obtain historical abnormal records (such as voltage sag, line tripping) and corresponding repair records (such as circuit breaker action time and load removal) for the past three years. Abstract distribution network equipment (such as transformer T1 and feeder L5) as graph nodes, and associate their electrical parameters (impedance, capacity) with timing states (such as the load rate of T1 at 10:00 on August 15, 2023 is 85%). For voltage abnormal events caused by a typhoon, take the abnormal time (14:30 on September 1, 2023) as the center, and expand 30 minutes forward and backward to form a time domain slice (14:00-15:00). The texture information (such as voltage deviation ±7%) and primitive information (topological connection between L5 and T1) of each device in the slice are extracted to generate an abnormal image, and the abnormal type is marked as "voltage exceeding the limit due to new energy fluctuations."

[0025] Furthermore, by comparing the current anomaly portrait with the historical portrait (such as similar events caused by the typhoon in July 2022), the scene similarity calculated based on texture information (voltage deviation trend matching degree 92%) and anomaly type (all voltage over-limit) is 0.88 (threshold 0.75). Attribute similarity calculation: The attribute similarity calculated by the primitive information (topology connection consistency 95%, same equipment type) is 0.91. Fusion of the dual similarity results (weighted score 0.89) determines that the anomaly boundary covers feeders L5, L6 and adjacent nodes N12-N15, and the repair window is predicted to be "need to cut off L5 load within 15 minutes and start energy storage compensation". The anomaly map shows the highest risk level (red) in the L5 area in the form of a heat map, and recommends a priority repair strategy.

[0026] S2. Expand the native database through the exception map and extract the texture information and graphic element information in the expanded target database to build a distribution network knowledge graph.

[0027] As an optional embodiment, S2 includes the following steps: Acquire the topological structure data and grid operation data of the distribution network during normal operation to build a native database; extract the time domain information matching the abnormal points in the native database, extract the texture information and graphic element information of the time domain section information corresponding to the abnormal point diagram, and determine the abnormal data based on the topological structure and flow direction with the abnormal point as the center; expand the native database according to the abnormal data to obtain the target database; extract the texture information in the target database to determine the causal chain and the graphic element information to determine the rule chain; determine the distribution network knowledge graph corresponding to the abnormal point according to the causal chain and the rule chain.

[0028] It is understandable that for the above-mentioned wind farm grid-connected area, the native database contains 500 device nodes, 2,000 topological connection relationships and SCADA real-time measurement data. The topological structure exported by the GIS system (such as L5 connecting T1 and wind turbine group WF1) is associated with SCADA measurement data (such as WF1 output fluctuation range 0-5MW) and stored. Match the time domain section information of the current abnormal point L5, extract its texture information (voltage fluctuation rate 12% / min) and graphic element information (downstream connection energy storage system ESS1).

[0029] According to the power flow direction (L5→ESS1), a causal chain of "new energy output fluctuation→L5 overload→ESS1 charging and discharging response delay" is constructed and stored in the target database. Based on the connection relationship of the graph elements, the rule is defined: "If the L5 load rate>90% and ESS1SOC<20%, the load transfer is triggered". New abnormal data (such as L5 overload events) are added to the graph in the form of triples (L5, hasFaultType, VoltageSag) and associated with historical events (similar faults in 2022). When SCADA detects a sudden increase in the L5 load rate, the knowledge graph automatically activates the association rule and pushes the "start ESS1 compensation" suggestion to the dispatching system.

[0030] S3. Determine the abnormal symbiosis area based on the abnormal situation and the knowledge graph of the distribution network, obtain the load transfer path based on the abnormal transition factor of the abnormal symbiosis area, analyze the transfer situation of the load transfer path and determine the target transfer strategy.

[0031] As an optional embodiment, the method of determining the abnormal co-existence area according to the abnormal situation in combination with the distribution network knowledge graph includes the following steps: Extract the three-dimensional features of the current abnormal point, and index the historical abnormal records associated with the device nodes with the same name according to the distribution network knowledge graph; activate the time dimension label in the graph to match the time domain features corresponding to the current abnormal point; Taking the abnormal point as the center, calculate the Euclidean distance of all devices in the set area, and build the spatial proximity distance according to the topological structure; traverse the connection relationship and logical relationship in the knowledge graph to identify the abnormal propagation path; Construct a symbiotic feature library based on the homology and spatial proximity of devices; calculate the Jaccard coefficient of the current abnormal combination and the historical pattern in turn, and activate the initial symbiotic area range of the corresponding pattern based on the Jaccard coefficient; The state estimation factor of the symbiotic area is determined according to the abnormal situation and the health of the equipment, and the boundary of the initial symbiotic area is adjusted according to the state estimation factor to obtain the abnormal symbiotic area.

[0032] In this embodiment, it is assumed that a sudden drop in the output of a photovoltaic power station causes an abnormal voltage on feeder L7, and a transfer path needs to be generated quickly; the spatial coordinates (x=32.5, y=45.2), timestamp (11:20 on October 10, 2023) and electrical characteristics (voltage drops to 0.9pu) of the abnormal point L7 are extracted. Through the knowledge graph, the historical abnormal records of the device with the same name L7 are retrieved, and it is found that it has the same voltage characteristics due to lightning strikes in June 2023, and the initial symbiotic area covers L7, L8 and circuit breaker CB3. The Jaccard coefficient of the current abnormal combination (L7 voltage abnormality + CB3 health 72%) and the historical pattern is calculated to be 0.82, and the symbiotic area is extended to the adjacent capacitor C1 (state estimation factor = 0.75×health+0.25×load rate=0.68).

[0033] As an optional embodiment, a load transfer path is obtained according to the abnormal transition factor of the abnormal symbiotic area, and a transfer situation analysis is performed on the load transfer path to determine a target transfer strategy; the steps include: The equipment health, historical transfer rate, and real-time load rate of the equipment in the abnormal symbiosis area are extracted from the distribution network knowledge graph and combined with the corresponding weight coefficient to determine the equipment abnormal transition factor; Taking the abnormal symbiotic area as the starting point, search for all device nodes that have contact with the area in the distribution network knowledge graph, and use the device nodes as candidate load transfer paths; A multi-dimensional transfer situation assessment index system is constructed by reliability index, economic index, timeliness index and risk index to calculate the comprehensive score of each candidate load transfer path, and the candidate load transfer path with the highest comprehensive score is taken as the target transfer path.

[0034] In this embodiment, the device health (weight 0.6), historical transfer rate (0.3), and real-time load rate (0.1) are calculated for the devices in the symbiotic area: CB3: health 72%×0.6 + transfer rate 85%×0.3 + load rate 88%×0.1 = 76.3%; C1: health 65%×0.6 + transfer rate 70%×0.3 + load rate 50%×0.1 = 64.5%; path screening: only select devices with transition factors>70% to participate in the transfer.

[0035] As an optional embodiment, starting from the abnormal symbiotic area, searching for all device nodes that have contact with the area in the distribution network knowledge graph, and taking the device nodes as candidate load transfer paths; the steps include: The constraint conditions of the path search are set, and the constraint conditions at least include the capacity limit of the tie switch and the voltage deviation threshold constraint; the improved Dijkstra algorithm is used to take the inverse of the abnormal transition factor as the heuristic information of the path search, and the shortest path to each potential transfer path node is gradually searched from the starting point; during the search process, whether the path meets the constraint conditions is continuously checked, and if not, the path is discarded; for the searched paths, the sum of the abnormal transition factors of all devices on each path is calculated, and the first n paths with the smallest sum of abnormal transition factors are selected as candidate load transfer paths.

[0036] In this embodiment, the capacity of the tie switch is limited (≤1000A), the voltage deviation threshold is (±10%); the inverse of the transition factor (1 / 76.3≈0.013) is used as the search weight, and the tie nodes (such as L9 and ESS2) are traversed from L7; this embodiment searches for two candidate paths: Path 1: L7→CB3→L9 (total transition factor 76.3+81.2=157.5, time consumption 12s); Path 2: L7→C1→ESS2 (total transition factor 64.5+89.1=153.6, time consumption 15s). Table 1 shows the comprehensive score calculation table of the path.

[0037] Table 1. Comprehensive score calculation table

[0038] As can be seen from Table 1, path 1 is selected as the target transfer path, and control instructions are automatically sent to switches CB3 and L9.

[0039] As an optional embodiment, the historical power transfer records of the devices on the path are obtained from the distribution network knowledge graph, and the ratio of the number of successful power transfers to the total number of power transfers is counted to calculate the historical power transfer success rate to obtain the reliability index; Obtain the line resistance, transfer current and transfer time on the path from the distribution network knowledge graph to determine the transfer power loss and obtain the economic index; Obtain the switch action time of the equipment on the path, the operation time of the switch equipment, and calculate the total time required for the entire transfer process to obtain the timeliness index; The transfer risk value is calculated according to the sum of abnormal transition factors of equipment on the path, load fluctuation rate and their corresponding weights to obtain the risk index.

[0040] Embodiment 2: In the second aspect, a technical solution provided in the embodiment of the present invention is: a new energy grid-connected path optimization configuration system, such as Figure 2 As shown, including: Determination module 101: acquiring power grid operation data and performing scene similarity analysis and attribute similarity analysis on the data to determine an abnormal map; Construction module 102: Expand the native database through the abnormal map and extract the texture information and graphic element information in the expanded target database to construct the distribution network knowledge graph; Generation module 103: Determine the abnormal symbiosis area according to the abnormal situation and the distribution network knowledge graph, obtain the load transfer path according to the abnormal transition factor of the abnormal symbiosis area, and perform transfer situation analysis on the load transfer path to determine the target transfer strategy.

[0041] Embodiment 3: In the third aspect, a technical solution provided in the embodiment of the present invention is: comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the new energy grid-connected path optimization configuration method based on knowledge graph technology are implemented.

[0042] Example 4: An optional embodiment also provided in the embodiments of the present invention is: a storage medium, which stores computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, the steps of the new energy grid-connected path optimization configuration method based on knowledge graph technology are implemented.

[0043] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0044] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, structures or units, which can be electrical, mechanical or other forms.

[0045] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0046] In addition, each functional unit in the embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0047] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0048] The specific implementation described above is a preferred implementation of the new energy grid-connected path optimization configuration method and system based on knowledge graph technology of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A new energy grid connection path optimization configuration method based on knowledge graph technology, characterized by: The steps include: Obtain power grid operation data and perform scene similarity analysis and attribute similarity analysis on it to determine the abnormal map; The native database is expanded through the abnormal map and the texture information and graphic element information in the expanded target database are extracted to build the distribution network knowledge graph; According to the abnormal situation and the knowledge graph of the distribution network, the abnormal symbiosis area is determined, and the load transfer path is obtained according to the abnormal transition factor of the abnormal symbiosis area. The load transfer situation is analyzed on the load transfer path to determine the target transfer strategy.

2. The new energy grid connection path optimization configuration method based on knowledge graph technology according to claim 1 is characterized in that: Obtaining power grid operation data and performing scene similarity analysis and attribute similarity analysis on it to determine an abnormal map; including the following steps: Obtain the abnormal records and corresponding repair records of the distribution network to determine the abnormal type, texture information and element information corresponding to each element in the time dimension; The abnormal scene of the distribution network is sliced ​​in the time domain based on the abnormal point to determine the abnormal portrait corresponding to each time domain slice; Determine the scene similarity of the abnormal image based on texture information and abnormal type; Determine the attribute similarity of the abnormal image based on the image element information and the abnormal type; The anomaly boundary and repair window of each time domain section are determined by scene similarity and attribute similarity, and an anomaly map is constructed based on the anomaly boundary and repair window.

3. The new energy grid connection path optimization configuration method based on knowledge graph technology according to claim 1 is characterized in that: The construction process of the distribution network knowledge graph includes: Acquire the topological structure data and grid operation data of the distribution network during normal operation to build a native database; extract the time domain information matching the abnormal points in the native database, extract the texture information and graphic element information of the time domain section information corresponding to the abnormal point diagram, and determine the abnormal data based on the topological structure and flow direction with the abnormal point as the center; expand the native database according to the abnormal data to obtain the target database; extract the texture information in the target database to determine the causal chain and the graphic element information to determine the rule chain; determine the distribution network knowledge graph corresponding to the abnormal point according to the causal chain and the rule chain.

4. The new energy grid connection path optimization configuration method based on knowledge graph technology according to claim 1 is characterized in that: The method of determining the abnormal co-existence area according to the abnormal situation and the distribution network knowledge graph includes the following steps: Extract the three-dimensional features of the current abnormal point, and index the historical abnormal records associated with the device nodes with the same name according to the distribution network knowledge graph; activate the time dimension label in the graph to match the time domain features corresponding to the current abnormal point; Taking the abnormal point as the center, calculate the Euclidean distance of all devices in the set area, and build the spatial proximity distance according to the topological structure; traverse the connection relationship and logical relationship in the knowledge graph to identify the abnormal propagation path; Construct a symbiotic feature library based on device homology and spatial proximity characteristics; The Jaccard coefficients of the current anomaly combination and the historical pattern are calculated in turn, and the initial symbiotic region range of the corresponding pattern is activated according to the Jaccard coefficient; The state estimation factor of the symbiotic area is determined according to the abnormal situation and the health of the equipment, and the boundary of the initial symbiotic area is adjusted according to the state estimation factor to obtain the abnormal symbiotic area.

5. The new energy grid connection path optimization configuration method based on knowledge graph technology according to claim 1 or 4 is characterized in that: The load transfer path is obtained according to the abnormal transition factor of the abnormal symbiotic area, and the load transfer situation analysis is performed on the load transfer path to determine the target transfer strategy; the steps include: The equipment health, historical transfer rate, and real-time load rate of the equipment in the abnormal symbiosis area are extracted from the distribution network knowledge graph and combined with the corresponding weight coefficient to determine the equipment abnormal transition factor; Taking the abnormal symbiotic area as the starting point, search for all device nodes that have contact with the area in the distribution network knowledge graph, and use the device nodes as candidate load transfer paths; A multi-dimensional transfer situation assessment index system is constructed by reliability index, economic index, timeliness index and risk index to calculate the comprehensive score of each candidate load transfer path, and the candidate load transfer path with the highest comprehensive score is taken as the target transfer path.

6. The new energy grid connection path optimization configuration method based on knowledge graph technology according to claim 5 is characterized in that: Taking the abnormal symbiotic area as the starting point, searching all device nodes that have contact with the area in the distribution network knowledge graph, and taking the device nodes as candidate load transfer paths; including the following steps: The constraint conditions of the path search are set, and the constraint conditions at least include the capacity limit of the tie switch and the voltage deviation threshold constraint; the improved Dijkstra algorithm is used to take the inverse of the abnormal transition factor as the heuristic information of the path search, and the shortest path to each potential transfer path node is gradually searched from the starting point; during the search process, whether the path meets the constraint conditions is continuously checked, and if not, the path is discarded; for the searched paths, the sum of the abnormal transition factors of all devices on each path is calculated, and the first n paths with the smallest sum of abnormal transition factors are selected as candidate load transfer paths.

7. The new energy grid connection path optimization configuration method based on knowledge graph technology according to claim 5 is characterized in that: Obtain the historical power transfer records of the equipment on the path from the distribution network knowledge graph, calculate the ratio of successful power transfer times to the total power transfer times, and calculate the historical power transfer success rate to obtain the reliability index; Obtain the line resistance, transfer current and transfer time on the path from the distribution network knowledge graph to determine the transfer power loss and obtain the economic index; Obtain the switch action time of the equipment on the path, the operation time of the switch equipment, and calculate the total time required for the entire transfer process to obtain the timeliness index; The transfer risk value is calculated according to the sum of abnormal transition factors of equipment on the path, load fluctuation rate and their corresponding weights to obtain the risk index.

8. A new energy grid-connected path optimization configuration system, applicable to the new energy grid-connected path optimization configuration method based on knowledge graph technology as described in any one of claims 1 to 7, characterized in that: include: Determination module: obtains power grid operation data and performs scene similarity analysis and attribute similarity analysis to determine the abnormal map; Construction module: Expand the native database through the abnormal map and extract the texture information and graphic element information in the expanded target database to build the distribution network knowledge graph; Generation module: Determine the abnormal symbiosis area based on the abnormal situation and the distribution network knowledge graph, obtain the load transfer path based on the abnormal transition factor of the abnormal symbiosis area, analyze the transfer situation of the load transfer path and determine the target transfer strategy.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the new energy grid-connected path optimization configuration method based on knowledge graph technology as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by the processor, implement the steps of the new energy grid-connected path optimization configuration method based on knowledge graph technology as described in any one of claims 1 to 7.

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