Power distribution system data visualization analysis system based on source network load storage

Abstract

The invention relates to the technical field of power distribution system data visualization, and relates to a power distribution system data visualization analysis system based on source network load storage. Historical power supply data of a power supply party is analyzed through a power supply quantity fluctuation index analysis module, and power supply quantity fluctuation is evaluated; and the load power consumption fluctuation index analysis module analyzes the power consumption fluctuation through the load power consumption data. And the fluctuation abnormity visualization module identifies and visualizes an abnormal power supply party and a load. The node health coefficient analysis module collects power distribution network node data and evaluates node health conditions. And the power distribution network visualization module marks abnormal nodes and circuits. And the regional anomaly degree index analysis module divides power grid regions, analyzes abnormal nodes and circuit density of each region, and determines the anomaly degree. And the abnormal region visualization module screens and combines the abnormal regions, and sorts the whole region area. The modules jointly form a set of complete power distribution network anomaly analysis and visualization system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution system data visualization, and relates to a power distribution system data visualization analysis system based on source, grid, load and storage. Background Art

[0002] Integrated source-grid-load-storage technology emphasizes the coordinated optimization of power sources, power grids, loads, and energy storage. By integrating renewable energy, smart grids, demand-side management, and energy storage technologies, it achieves efficient, reliable, and sustainable operation of the power system. The application of this integrated technology makes data analysis of distribution systems more complex, but also provides more optimization and control methods.

[0003] The existing data visualization and analysis system for distribution systems based on source, grid, load and storage has some shortcomings: First, the existing technology is often unable to quickly and accurately identify abnormal power suppliers and abnormal loads, and thus cannot intuitively and clearly display abnormal data, resulting in an inability to accurately reflect the distribution and patterns of abnormal power suppliers and abnormal loads.

[0004] Secondly, existing technologies are insufficient in analyzing inflow and outflow nodes to identify abnormal circuits. Traditional analysis methods can only focus on the status of a single node, while ignoring the correlation and mutual influence between nodes. Therefore, existing technologies are difficult to accurately determine each abnormal circuit and cannot fully reflect the health status of the distribution network.

[0005] Third, the positioning of abnormal areas is not accurate enough. Operation and maintenance personnel may need to check the entire distribution network one by one and then gradually narrow the scope. As a result, they cannot prioritize the abnormal areas according to their severity in a timely manner and cannot give priority to abnormal areas with higher potential risks. This may not only lead to irrational resource allocation, but also affect the safe and stable operation of the distribution network. Summary of the Invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a distribution system data visualization analysis system based on source, grid, load and storage is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a distribution system data visualization analysis system based on source, grid, load and storage, including: a database: used to store the historical power supply data of each power supplier, the historical power consumption data of each load, the rated voltage of each node in the distribution network, and the rated power of each node in the distribution network.

[0008] Power supply fluctuation index analysis module: used to extract the power supply of each power supplier in each historical day and time period within a set historical period through the historical power supply data of each power supplier, and then analyze the power supply fluctuation index of each power supplier.

[0009] Load power usage fluctuation index analysis module: used to extract the power usage of each load on each historical day within a set historical period through the historical power usage data of each load, and then analyze the power usage fluctuation index of each load.

[0010] Fluctuation abnormality visualization module: used to screen out abnormal power suppliers and abnormal loads and visualize them according to the power supply fluctuation index of each power supplier and the power consumption fluctuation index of each load.

[0011] Node health factor analysis module: used to collect voltage and power factors of each node in the distribution network and analyze the health factor of each node in the distribution network.

[0012] Distribution network visualization module: It is used to screen out abnormal nodes in the distribution network through the health coefficient of each node in the distribution network, and screen out abnormal circuits in the distribution network based on the abnormal nodes in the distribution network, and then color-mark the abnormal nodes and abnormal circuits in the distribution network.

[0013] Regional abnormality index analysis module: It is used to divide the distribution network into regions according to the set area to obtain the distribution network regions, and analyze the abnormal node density index K of each region of the distribution network through the abnormal nodes and abnormal circuits in each region of the distribution network. q and the abnormal circuit density index P in each area of ​​the distribution network q , and then analyze the abnormality index of each area of ​​the distribution network.

[0014] Abnormal area visualization module: used to screen abnormal areas of the distribution network through the abnormal degree index of each area of ​​the distribution network, and then merge each abnormal area of ​​the distribution network with its corresponding adjacent abnormal areas, and sort the overall area of ​​each abnormal area of ​​the distribution network.

[0015] Preferably, the analysis of the power supply fluctuation index of each power supplier includes: extracting the power supply of each power supplier in each time period of each historical day within the set historical period, and recording it as Where g represents the number corresponding to different power suppliers, g = 1, 2, 3...G, where r represents the number corresponding to different historical days within the set historical period, r = 1, 2, 3...R, where t represents the number corresponding to different time periods, t = 1, 2, 3...T, analyze the power supply fluctuation index of each power supplier in It represents the average power supply of the g-th power supplier in the t-th time period within the set historical period. T represents the total number of time periods, R represents the total number of historical days in the set historical period, and ΔE0 represents the allowable error value of power supply in the set time period.

[0016] Preferably, the analysis of the power consumption fluctuation index of each load includes: extracting the power consumption of each load on each historical day within the set historical period, comparing the power consumption of each load on each historical day within the set historical period with the power consumption of its adjacent historical day, obtaining the power consumption difference between each load on each historical day within the set historical period and its adjacent historical day, and recording it as Where f represents the number corresponding to different loads, f = 1, 2, 3...F, and analyzes the power consumption fluctuation index of each load ΔE0′ is the allowable error value of load power usage between a set historical day and its adjacent historical days.

[0017] Preferably, the abnormal power suppliers are visualized, including: comparing the power supply fluctuation index of each power supplier with a preset power supply fluctuation abnormality threshold; if the power supply fluctuation index of a power supplier is greater than or equal to the preset power supply fluctuation abnormality threshold, the power supplier is an abnormal power supplier; the abnormal power suppliers are screened out and then the corresponding numbers of the abnormal power suppliers are obtained, and the corresponding numbers of the abnormal power suppliers are visualized.

[0018] Preferably, the analysis of the health coefficient of each node in the distribution network includes: collecting the voltage U of each node in the distribution network through a smart meter i , where i represents the number corresponding to different nodes in the distribution network, i = 1, 2, 3...I, the rated voltage U' of the distribution network node is extracted from the database, and the power factor p of each node in the distribution network is collected through the power factor table i , obtain the rated power of each node in the distribution network from the database The specific formula for analyzing the health coefficient of each node in the distribution network is: Φ i =α1·E i +α2·E′ i , where E i represents the voltage stability index of the ith node in the distribution network, U0 represents the allowable deviation value of the set distribution network node voltage, where E' i represents the power factor stability index of the i-th node in the distribution network, Wherein, α1 represents a set weight factor of the voltage stability index, α2 represents a set weight factor of the power factor stability index, and α1+α2=1.

[0019] Preferably, the color marking of each abnormal node in the distribution network includes: comparing the health coefficient of each node in the distribution network with a preset qualified health threshold; if the health coefficient of a node in the distribution network is less than the preset qualified health threshold, marking the node in the distribution network as an abnormal node in the distribution network; if the health coefficient of a node in the distribution network is greater than or equal to the preset qualified health threshold, marking the node in the distribution network as a normal node in the distribution network, screening out the abnormal nodes in the distribution network, and then obtaining the numbers of the abnormal nodes in the distribution network, obtaining the topological structure of the distribution network through power system simulation software, obtaining the positions of the nodes in the distribution network through geographic information software, importing the numbers of the abnormal nodes in the distribution network into the simulation software of the distribution network, constructing a topological map of the distribution network through the simulation software of the distribution network, and then color marking the abnormal nodes in the distribution network.

[0020] Preferably, the color marking of each abnormal circuit in the distribution network includes: obtaining the inflow node and outflow node of each circuit through the topological map of the distribution network, analyzing the inflow node and outflow node of each circuit, if the inflow node of a circuit is a normal node of the distribution network and the outflow node is an abnormal node of the distribution network, then marking the circuit as an abnormal circuit in the topological map of the distribution network; if the inflow node of a circuit is a normal node of the distribution network and the outflow node is a normal node of the distribution network, then marking the circuit as a normal circuit in the topological map of the distribution network; if the inflow node of a circuit is an abnormal node of the distribution network and the outflow node is an abnormal node of the distribution network, then marking the circuit as an abnormal circuit in the topological map of the distribution network; if the inflow node of a circuit is an abnormal node of the distribution network and the outflow node is an abnormal node of the distribution network, then marking the circuit as a normal circuit in the topological map of the distribution network, screening out each abnormal circuit in the distribution network, and then color marking each abnormal circuit in the distribution network.

[0021] Preferably, the analysis of the abnormal node density index of each area of ​​the distribution network includes: dividing the distribution network into regions according to a set area to obtain each area of ​​the distribution network, setting a target point in the node of each area of ​​the distribution network, and setting other nodes in each area of ​​the distribution network as reference points, obtaining the distance from the target point to each reference point in each area of ​​the distribution network through a topological map of the distribution network, comparing the distance from the target point to each reference point in each area of ​​the distribution network with a preset dense node distance, if the distance from the target point to a certain reference point is less than or equal to the preset dense node distance, then recording the reference point as a dense node, screening and counting the number of dense nodes χq in each area of ​​the distribution network, and screening and counting the number of abnormal nodes δ in each area of ​​the distribution network. q , and then calculate the total number of nodes in each area of ​​the distribution network δ q ′, analyze the abnormal node density index K in each area of ​​the distribution network q =K′ q ·τ1+K′ q′·τ2, where K′ q represents the proportion index of abnormal nodes in the qth area of ​​the distribution network, K′ q ′ represents the density of abnormal nodes in the qth area of ​​the distribution network, τ1 represents the weight factor of the distribution network abnormal node ratio index, τ2 represents the weight factor of the distribution network abnormal node density, and τ1+τ2=1, where D represents the total number of distances from the target point to different reference points.

[0022] Preferably, the specific formula for analyzing the abnormality index of each area of ​​the distribution network is: q =K q β1+Ρ q β2, where K q represents the abnormal node density index of the qth area of ​​the distribution network, P q represents the abnormal circuit density index of the qth area of ​​the distribution network, β1 represents the weight factor of the set abnormal node density index, β2 represents the weight factor of the set abnormal circuit density index, and β1+β2=1.

[0023] Preferably, the sorting of the areas of the abnormal overall regions of the distribution network includes: comparing the abnormality degree index of each region of the distribution network with a preset abnormality degree threshold; if the abnormality degree index of a region of the distribution network is greater than the preset abnormality degree threshold, then the region of the distribution network is an abnormal region, and the abnormal regions of the distribution network are screened out.

[0024] The simulation software of the distribution network is used to obtain the distances between the center points of each abnormal area of ​​the distribution network and its corresponding adjacent abnormal areas. The topological map of the distribution network is then used to obtain the distances between the center points of each abnormal area of ​​the distribution network and its corresponding adjacent abnormal areas. The adjacent abnormal areas whose corresponding center point distances of each abnormal area of ​​the distribution network are less than or equal to the preset adjacent distance are counted, and the adjacent abnormal areas are merged with the corresponding abnormal areas into abnormal overall areas, thereby obtaining the abnormal overall areas of the distribution network. The areas of the abnormal overall areas of the distribution network are obtained through the simulation software of the distribution network, and the abnormal overall areas of the distribution network are sorted according to their area sizes.

[0025] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention quickly identifies abnormal power suppliers and abnormal loads in the distribution network by comparing the power supply fluctuation index of each power supplier and the power consumption fluctuation index of each load with the preset power supply fluctuation abnormality threshold and power consumption fluctuation abnormality threshold, respectively. This helps to promptly discover and resolve potential problems, prevent faults from expanding or affecting the operation of the entire distribution network, and visualize the numbering of abnormal power suppliers and abnormal loads, providing intuitive data support for decision makers. By analyzing the distribution and patterns of abnormal power suppliers and abnormal loads, targeted optimization measures and improvement plans can be formulated to improve the management level and operation efficiency of the distribution network.

[0026] (2) The present invention can more accurately grasp the operating status of the distribution network by extracting and analyzing the voltage and power factors of each node in the distribution network in real time, which helps to timely discover and solve potential problems and ensure the safe and stable operation of the distribution network. The continuous monitoring and analysis of the health factors of each node in the distribution network can predict and prevent potential faults and accidents, help to reduce power outages caused by faults, reduce maintenance costs, and improve user satisfaction. In addition, operation and maintenance personnel can more accurately locate problem nodes, reduce troubleshooting time, and improve operation and maintenance efficiency.

[0027] (3) The present invention can accurately identify abnormal nodes in the distribution network by comparing the node health coefficient with the qualified health threshold. Furthermore, combined with the analysis of the inflow and outflow nodes, each abnormal circuit can be accurately determined. This analysis method can comprehensively and accurately reflect the health status of the distribution network. By marking abnormal nodes and abnormal circuits, operation and maintenance personnel can directly locate potential problem areas without having to check the entire distribution network one by one. This greatly reduces the time for troubleshooting, improves work efficiency, and reduces unnecessary waste of resources.

[0028] (4) By calculating the abnormal node density index and abnormal circuit density index of each area, the present invention can intuitively assess the health status of each area. This assessment method helps managers understand the operational risks of each area and provides a basis for formulating risk prevention and control measures. Based on the analysis of the abnormality index of each area, maintenance resources can be more reasonably allocated. For areas with high abnormality levels, inspection and maintenance efforts can be strengthened to ensure the stability of power supply. For areas with low abnormality levels, the inspection frequency can be appropriately reduced to reduce operation and maintenance costs.

[0029] (5) The present invention can accurately identify abnormal areas by comparing the abnormality index of each area of ​​the distribution network with the preset abnormality threshold. This method avoids misjudgment caused by subjective judgment or empiricism, improves the accuracy of abnormal area identification, and merges and sorts the identified abnormal areas to achieve unified management of abnormal areas. Merging adjacent abnormal areas helps to reduce management units and reduce management costs. Sorting by area size allows managers to give priority to abnormal areas with larger areas and higher potential risks, thereby optimizing resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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.

[0031] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1 As shown, the present invention provides a distribution system data visualization analysis system based on source-grid-load-storage, including: a database, a power supply fluctuation index analysis module, a load power usage fluctuation index analysis module, a fluctuation anomaly visualization module, a node health coefficient analysis module, a distribution network visualization module, a regional anomaly degree index analysis module and an abnormal area visualization module.

[0034] The database is connected to the power supply fluctuation index analysis module, the load power usage fluctuation index analysis module, and the node health coefficient analysis module respectively; the load power usage fluctuation index analysis module is connected to the power supply fluctuation index analysis module and the fluctuation anomaly visualization module respectively; the distribution network visualization module is connected to the node health coefficient analysis module and the regional anomaly degree index analysis module respectively; and the abnormal area visualization module is connected to the regional anomaly degree index analysis module.

[0035] Database: used to store the historical power supply data of each power supplier, the historical power consumption data of each load, the rated voltage of each node in the distribution network, and the rated power of each node in the distribution network.

[0036] The power supply fluctuation index analysis module is used to extract the power supply of each power supplier in each time period of each historical day within a set historical period through the historical power supply data of each power supplier, and then analyze the power supply fluctuation index of each power supplier.

[0037] Furthermore, the analysis of the power supply fluctuation index of each power supplier includes: extracting and setting

[0038] The power supply of each power supplier in each historical day and time period during the historical period is recorded as Where g represents the number corresponding to different power suppliers, g = 1, 2, 3...G, where r represents the number corresponding to different historical days within the set historical period, r = 1, 2, 3...R, where t represents the number corresponding to different time periods, t = 1, 2, 3...T, analyze the power supply fluctuation index of each power supplier in It represents the average power supply of the g-th power supplier in the t-th time period within the set historical period. T represents the total number of time periods, R represents the total number of historical days in the set historical period, and ΔE0 represents the allowable error value of power supply in the set time period.

[0039] By conducting in-depth analysis of power supply by each power supplier at each time period within a historical period, this invention can more accurately understand the changing trends and fluctuation patterns of power supply. This helps power suppliers make more precise operational decisions, such as adjusting power supply plans and optimizing resource allocation, thereby improving power supply efficiency and economic benefits. By analyzing the power supply fluctuation index, it is possible to predict future changes in power demand and optimize power dispatch and allocation strategies. This helps reduce power shortages or surpluses and ensure the stability and reliability of power supply.

[0040] The load power usage fluctuation index analysis module is used to extract the power usage of each load on each historical day within a set historical period through the historical power usage data of each load, and then analyze the power usage fluctuation index of each load.

[0041] Furthermore, the analysis of the power consumption fluctuation index of each load includes: extracting the power consumption of each load on each historical day within the set historical period, comparing the power consumption of each load on each historical day within the set historical period with the power consumption of its adjacent historical day, obtaining the power consumption difference between each load on each historical day within the set historical period and its adjacent historical day, and recording it as Where f represents the number corresponding to different loads, f = 1, 2, 3...F, and analyzes the power consumption fluctuation index of each load ΔE0′ is the allowable error value of load power usage between a set historical day and its adjacent historical days.

[0042] By comparing power usage on adjacent historical days, this method can accurately understand the changes in power usage behavior of each load within a set historical period. This helps identify the load's power usage pattern, periodicity, and abnormal fluctuations, providing an important basis for subsequent power management. Furthermore, by analyzing the power usage fluctuation index of each load, it is possible to understand the power usage characteristics, demand, and changing trends of different loads. This helps to formulate more accurate load management strategies, optimize resource allocation, and improve the operating efficiency and economic efficiency of the power system.

[0043] Fluctuation abnormality visualization module: used to screen out abnormal power suppliers and abnormal loads and visualize them according to the power supply fluctuation index of each power supplier and the power consumption fluctuation index of each load.

[0044] Furthermore, the abnormal power suppliers are visualized, including: comparing the power supply fluctuation index of each power supplier with a preset power supply fluctuation abnormality threshold; if the power supply fluctuation index of a power supplier is greater than or equal to the preset power supply fluctuation abnormality threshold, the power supplier is an abnormal power supplier; the abnormal power suppliers are screened out and the corresponding numbers of the abnormal power suppliers are obtained; and the corresponding numbers of the abnormal power suppliers are visualized.

[0045] Furthermore, the abnormal loads are visualized, including: comparing the power usage fluctuation index of each load with a preset power usage fluctuation abnormal threshold; if the power usage fluctuation index of a load is greater than or equal to the preset power usage fluctuation abnormal threshold, the load is an abnormal load, and the abnormal loads are screened out and the numbers of the abnormal loads are visualized.

[0046] This invention rapidly identifies abnormal power suppliers and loads in the distribution network by comparing the power supply fluctuation index of each power supplier and the power usage fluctuation index of each load with preset power supply fluctuation anomaly thresholds and power usage fluctuation anomaly thresholds, respectively. This helps to promptly discover and resolve potential problems, preventing faults from expanding or affecting the operation of the entire distribution network. Visualizing the numbering of abnormal power suppliers and loads provides intuitive data support for decision makers. By analyzing the distribution and patterns of abnormal power suppliers and loads, targeted optimization measures and improvement plans can be formulated, improving the management level and operational efficiency of the distribution network.

[0047] The node health factor analysis module is used to collect the voltage and power factors of each node in the distribution network and analyze the health factor of each node in the distribution network.

[0048] Furthermore, the analysis of the health coefficient of each node in the distribution network includes: collecting the voltage U of each node in the distribution network through a smart meter i , where i represents the number corresponding to different nodes in the distribution network, i = 1, 2, 3...I, the rated voltage U' of the distribution network node is extracted from the database, and the power factor p of each node in the distribution network is collected through the power factor tablei , obtain the rated power of each node in the distribution network from the database The specific formula for analyzing the health coefficient of each node in the distribution network is: Φ i =α1·E i +α2·E′ i , where E i represents the voltage stability index of the ith node in the distribution network, U0 represents the allowable deviation value of the set distribution network node voltage, where E' i represents the power factor stability index of the i-th node in the distribution network, Wherein, α1 represents a set weight factor of the voltage stability index, α2 represents a set weight factor of the power factor stability index, and α1+α2=1.

[0049] It should be noted that, in one embodiment, α1 can be set to 0.5, and α2 can be set to 0.5. Voltage stability is one of the key factors for the stable operation of the power system. Voltage fluctuations may cause equipment failure, affect the power supply quality, and even cause the collapse of the entire system. However, power factor stability is also an important indicator in the power system. The power factor reflects the proportional relationship between active power and apparent power in the circuit, which directly affects the utilization efficiency of electric energy and the operating status of the power grid. Therefore, the weight factor of the voltage stability index is equal to the weight factor of the power factor stability index.

[0050] By extracting and analyzing the voltage and power factors of each node in the distribution network in real time, the present invention can more accurately grasp the operating status of the distribution network, which helps to promptly discover and solve potential problems and ensure the safe and stable operation of the distribution network. Continuous monitoring and analysis of the health coefficient of each node in the distribution network can predict and prevent potential failures and accidents, help to reduce power outages caused by failures, reduce maintenance costs, and improve user satisfaction. In addition, operation and maintenance personnel can more accurately locate problem nodes, reduce troubleshooting time, and improve operation and maintenance efficiency.

[0051] Distribution network visualization module: It is used to screen out abnormal nodes in the distribution network through the health coefficient of each node in the distribution network, and screen out abnormal circuits in the distribution network based on the abnormal nodes in the distribution network, and then color-mark the abnormal nodes and abnormal circuits in the distribution network.

[0052] Furthermore, the color marking of each abnormal node in the distribution network includes: comparing the health coefficient of each node in the distribution network with a preset qualified health threshold; if the health coefficient of a node in the distribution network is less than the preset qualified health threshold, marking the node in the distribution network as an abnormal node in the distribution network; if the health coefficient of a node in the distribution network is greater than or equal to the preset qualified health threshold, marking the node in the distribution network as a normal node in the distribution network, screening out the abnormal nodes in the distribution network, and then obtaining the numbers of the abnormal nodes in the distribution network, obtaining the topological structure of the distribution network through power system simulation software, obtaining the positions of the nodes in the distribution network through geographic information software, importing the numbers of the abnormal nodes in the distribution network into the simulation software of the distribution network, constructing a topological map of the distribution network through the simulation software of the distribution network, and then color marking the abnormal nodes in the distribution network.

[0053] Furthermore, the color marking of each abnormal circuit in the distribution network includes: obtaining the inflow node and outflow node of each circuit through the topological map of the distribution network, analyzing the inflow node and outflow node of each circuit, if the inflow node of a circuit is a normal node of the distribution network and the outflow node is an abnormal node of the distribution network, then marking the circuit as an abnormal circuit in the topological map of the distribution network; if the inflow node of a circuit is a normal node of the distribution network and the outflow node is a normal node of the distribution network, then marking the circuit as a normal circuit in the topological map of the distribution network; if the inflow node of a circuit is an abnormal node of the distribution network and the outflow node is an abnormal node of the distribution network, then marking the circuit as an abnormal circuit in the topological map of the distribution network; if the inflow node of a circuit is an abnormal node of the distribution network and the outflow node is an abnormal node of the distribution network, then marking the circuit as a normal circuit in the topological map of the distribution network, screening out each abnormal circuit in the distribution network, and then color marking each abnormal circuit in the distribution network.

[0054] By comparing the node health coefficient with the qualified health threshold, the present invention can accurately identify abnormal nodes in the distribution network. Furthermore, combined with the analysis of the inflow and outflow nodes, each abnormal circuit can be accurately determined. This analysis method can comprehensively and accurately reflect the health status of the distribution network. By marking abnormal nodes and abnormal circuits, operation and maintenance personnel can directly locate potential problem areas without having to check the entire distribution network one by one. This greatly reduces the time for troubleshooting, improves work efficiency, and reduces unnecessary waste of resources.

[0055] Regional abnormality index analysis module: It is used to divide the distribution network into regions according to the set area to obtain the distribution network regions, and analyze the abnormal node density index K of each region of the distribution network through the abnormal nodes and abnormal circuits in each region of the distribution network. q and the abnormal circuit density index P in each area of ​​the distribution network q , and then analyze the abnormality index of each area of ​​the distribution network.

[0056] Furthermore, the analysis of the abnormal node density index of each area of ​​the distribution network includes: dividing the distribution network into regions according to a set area to obtain each area of ​​the distribution network, setting a target point in the node of each area of ​​the distribution network, and setting other nodes in each area of ​​the distribution network as reference points, obtaining the distance from the target point to each reference point in each area of ​​the distribution network through a topological map of the distribution network, comparing the distance from the target point to each reference point in each area of ​​the distribution network with a preset dense node distance, if the distance from the target point to a certain reference point is less than or equal to the preset dense node distance, then recording the reference point as a dense node, screening and counting the number of dense nodes χq in each area of ​​the distribution network, and screening and counting the number of abnormal nodes δ in each area of ​​the distribution network. q , and then calculate the total number of nodes in each area of ​​the distribution network δ q ′, analyze the abnormal node density index K in each area of ​​the distribution network q =K′ q ·τ1+K′ q ′·τ2, where K′ q represents the proportion index of abnormal nodes in the qth area of ​​the distribution network, K′ q ′ represents the density of abnormal nodes in the qth area of ​​the distribution network, τ1 represents the weight factor of the distribution network abnormal node ratio index, τ2 represents the weight factor of the distribution network abnormal node density, and τ1+τ2=1, where D represents the total number of distances from the target point to different reference points.

[0057] Furthermore, the analysis of the abnormal circuit density index of each area of ​​the distribution network includes: setting a target circuit in the circuit of each area of ​​the distribution network, and setting other circuits in each area of ​​the distribution network as reference circuits, obtaining a graph model of the target circuit and each reference circuit in each area of ​​the distribution network according to the topological map of the distribution network, obtaining the shortest path between the target circuit and each reference circuit in each area of ​​the distribution network in the graph model of the target circuit and each reference circuit in each area of ​​the distribution network by using the Dijkstra algorithm, comparing the shortest path between the target circuit and each reference circuit in each area of ​​the distribution network and the distance to a preset dense circuit, if the shortest path between the target circuit and a reference circuit is less than or equal to the preset dense circuit distance, then recording the reference circuit as a dense circuit, and screening and counting the number η′ of dense circuits in each area of ​​the distribution network. q , and then obtain the total number of circuits in each area of ​​the distribution network η′ q ′, analyze the abnormal circuit density index of each area of ​​the distribution network

[0058]

[0059] Furthermore, the specific formula for analyzing the abnormality index of each area of ​​the distribution network is: q =Kq β1+Ρ q β2, where K q represents the abnormal node density index of the qth area of ​​the distribution network, P q represents the abnormal circuit density index of the qth area of ​​the distribution network, β1 represents the weight factor of the set abnormal node density index, β2 represents the weight factor of the set abnormal circuit density index, and β1+β2=1.

[0060] It should be noted that, in one embodiment, β1 can be set to 0.5, and β2 can be set to 0.5. The density of abnormal nodes is one of the important indicators for evaluating the health of the distribution network. The concentrated appearance of abnormal nodes often means that there are more serious faults or potential risks in the area. However, only considering the density of nodes may not be enough to fully reflect the complexity and interrelationship of the distribution network. The density of abnormal circuits is also important for evaluating the health of the distribution network. Circuits are channels for power transmission in the distribution network. The concentrated appearance of abnormal circuits means that faults may occur simultaneously in multiple circuits, which will have a greater impact on the overall operation of the distribution network. Therefore, the weight factor of the abnormal circuit density index is greater than the weight factor of the abnormal node density index.

[0061] By calculating the abnormal node density index and abnormal circuit density index for each area, the present invention can intuitively assess the health status of each area. This assessment method helps managers understand the operational risks of each area and provides a basis for formulating risk prevention and control measures. Based on the analysis of the abnormality index of each area, maintenance resources can be more rationally allocated. For areas with high abnormality levels, inspections and maintenance efforts can be strengthened to ensure the stability of power supply. For areas with low abnormality levels, inspection frequency can be appropriately reduced to reduce operation and maintenance costs.

[0062] Abnormal area visualization module: used to screen abnormal areas of the distribution network through the abnormality degree index of each area of ​​the distribution network, and obtain and visualize the overall abnormal areas of the distribution network through the simulation software of the distribution network.

[0063] Furthermore, the ranking of the areas of the abnormal overall regions of the distribution network includes: comparing the abnormality degree index of each region of the distribution network with a preset abnormality degree threshold; if the abnormality degree index of a region of the distribution network is greater than the preset abnormality degree threshold, then the region of the distribution network is an abnormal region, and the abnormal regions of the distribution network are screened out.

[0064] The simulation software of the distribution network is used to obtain the distances between the center points of each abnormal area of ​​the distribution network and its corresponding adjacent abnormal areas. The topological map of the distribution network is then used to obtain the distances between the center points of each abnormal area of ​​the distribution network and its corresponding adjacent abnormal areas. The adjacent abnormal areas whose corresponding center point distances of each abnormal area of ​​the distribution network are less than or equal to the preset adjacent distance are counted, and the adjacent abnormal areas are merged with the corresponding abnormal areas into abnormal overall areas, thereby obtaining the abnormal overall areas of the distribution network. The areas of the abnormal overall areas of the distribution network are obtained through the simulation software of the distribution network, and the abnormal overall areas of the distribution network are sorted according to their area sizes.

[0065] The present invention can accurately identify abnormal areas by comparing the abnormality index of each area of ​​the distribution network with a preset abnormality threshold. This method avoids misjudgment caused by subjective judgment or empiricism, improves the accuracy of abnormal area identification, and merges and sorts the identified abnormal areas to achieve unified management of abnormal areas. Merging adjacent abnormal areas helps to reduce management units and reduce management costs. Sorting by area size allows managers to give priority to abnormal areas with larger areas and higher potential risks, thereby optimizing resource allocation.

[0066] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A data visualization analysis system for a power distribution system based on source, grid, load and storage, characterized by: include: Database: used to store historical power supply data of each power supplier, historical power consumption data of each load, rated voltage of each node in the distribution network, and rated power of each node in the distribution network; Power supply fluctuation index analysis module: used to extract the power supply of each power supplier in each historical day and time period within a set historical period through the historical power supply data of each power supplier, and then analyze the power supply fluctuation index of each power supplier; Load power usage fluctuation index analysis module: used to extract the power usage of each load on each historical day within a set historical period through the historical power usage data of each load, and then analyze the power usage fluctuation index of each load; Fluctuation abnormality visualization module: used to screen out abnormal power suppliers and abnormal loads and visualize them according to the power supply fluctuation index of each power supplier and the power consumption fluctuation index of each load; Node health factor analysis module: used to collect voltage and power factors of each node in the distribution network and analyze the health factor of each node in the distribution network; Distribution network visualization module: used to screen out abnormal nodes in the distribution network based on the health coefficient of each node, and screen out abnormal circuits in the distribution network based on the abnormal nodes, and then color-code the abnormal nodes and circuits in the distribution network; Regional anomaly index analysis module: used to divide the distribution network into regions according to the set area to obtain each region of the distribution network, and analyze the abnormal node density index K of each region of the distribution network respectively q and the abnormal circuit density index P in each area of ​​the distribution network q , where q represents the number corresponding to different areas of the distribution network, q = 1, 2, 3...Q, and then the abnormality index of each area of ​​the distribution network is analyzed; Abnormal area visualization module: used to screen abnormal areas of the distribution network through the abnormal degree index of each area of ​​the distribution network, and then merge each abnormal area of ​​the distribution network with its corresponding adjacent abnormal areas, and sort the overall area of ​​each abnormal area of ​​the distribution network.

2. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1 is characterized by: The analysis of the power supply fluctuation index of each power supplier includes: Extract the power supply of each power supplier in each historical day and time period within the set historical period and record it as Where g represents the number corresponding to different power suppliers, g = 1, 2, 3...G, where r represents the number corresponding to different historical days within the set historical period, r = 1, 2, 3...R, where t represents the number corresponding to different time periods, t = 1, 2, 3...T, analyze the power supply fluctuation index of each power supplier in It represents the average power supply of the g-th power supplier in the t-th time period within the set historical period. T represents the total number of time periods, R represents the total number of historical days in the set historical period, and ΔE0 represents the allowable error value of power supply in the set time period.

3. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 2 is characterized by: The analysis of the power usage fluctuation index of each load includes: Extract the power consumption of each load on each historical day within the set historical period, compare the power consumption of each load on each historical day within the set historical period with the power consumption of its adjacent historical day, and obtain the power consumption difference between each load on each historical day within the set historical period and its adjacent historical day, which is recorded as Where f represents the number corresponding to different loads, f = 1, 2, 3...F, and analyzes the power consumption fluctuation index of each load ΔE0′ is the allowable error value of load power usage between a set historical day and its adjacent historical days.

4. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1 is characterized by: The visualization of each abnormal power supply party includes: The power supply fluctuation index of each power supplier is compared with the preset power supply fluctuation abnormality threshold. If the power supply fluctuation index of a power supplier is greater than or equal to the preset power supply fluctuation abnormality threshold, the power supplier is considered an abnormal power supplier. The abnormal power suppliers are screened out and the corresponding numbers of the abnormal power suppliers are obtained, and the corresponding numbers of the abnormal power suppliers are visualized.

5. The data visualization analysis system for distribution system based on source, grid, load and storage according to claim 1 is characterized in that : The analysis of the health coefficient of each node in the distribution network includes: The voltage U of each node in the distribution network is collected by smart meters i , where i represents the number corresponding to different nodes in the distribution network, i = 1, 2, 3...I, the rated voltage U' of the distribution network node is extracted from the database, and the power factor p of each node in the distribution network is collected through the power factor table i , obtain the rated power of each node in the distribution network from the database The specific formula for analyzing the health coefficient of each node in the distribution network is: Φ i =α1·E i +α2·E′ i , where E i represents the voltage stability index of the ith node in the distribution network, U0 represents the allowable deviation value of the set distribution network node voltage, where E' i represents the power factor stability index of the i-th node in the distribution network, Wherein, α1 represents a set weight factor of the voltage stability index, α2 represents a set weight factor of the power factor stability index, and α1+α2=1.

6. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1 is characterized by: The color marking of each abnormal node in the distribution network includes: The health coefficient of each node in the distribution network is compared with the preset qualified health threshold. If the health coefficient of a node in the distribution network is less than the preset qualified health threshold, the node in the distribution network is marked as an abnormal node in the distribution network; if the health coefficient of a node in the distribution network is greater than or equal to the preset qualified health threshold, the node in the distribution network is marked as a normal node in the distribution network. The abnormal nodes in the distribution network are screened out, and the numbers of the abnormal nodes in the distribution network are obtained. The topological structure of the distribution network is obtained through the power system simulation software, and the positions of the nodes in the distribution network are obtained through the geographic information software. The numbers of the abnormal nodes in the distribution network are imported into the distribution network simulation software, and the topological map of the distribution network is constructed through the distribution network simulation software, and the abnormal nodes in the distribution network are color-coded.

7. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1 is characterized by: The color marking of each abnormal circuit in the distribution network includes: The inflow node and outflow node of each circuit are obtained through the topological map of the distribution network, and the inflow node and outflow node of each circuit are analyzed. If the inflow node of a circuit is a normal node of the distribution network and the outflow node is an abnormal node of the distribution network, the circuit is marked as an abnormal circuit in the topological map of the distribution network; if the inflow node of a circuit is a normal node of the distribution network and the outflow node is a normal node of the distribution network, the circuit is marked as a normal circuit in the topological map of the distribution network; if the inflow node of a circuit is an abnormal node of the distribution network and the outflow node is an abnormal node of the distribution network, the circuit is marked as an abnormal circuit in the topological map of the distribution network; if the inflow node of a circuit is an abnormal node of the distribution network and the outflow node is a normal node of the distribution network, the circuit is marked as a normal circuit in the topological map of the distribution network, and the abnormal circuits of the distribution network are screened out, and then the abnormal circuits of the distribution network are color-coded.

8. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1 is characterized by: The analysis of abnormal node density index in each area of ​​the distribution network includes: The distribution network is divided into regions according to the set area to obtain each region of the distribution network. A target point is set in the node of each region of the distribution network, and other nodes in each region of the distribution network are set as reference points. The distance from the target point to each reference point in each region of the distribution network is obtained through the topological map of the distribution network. The distance from the target point to each reference point in each region of the distribution network is compared with the preset dense node distance. If the distance from the target point to a certain reference point is less than or equal to the preset dense node distance, the reference point is recorded as a dense node. The number of dense nodes χq in each region of the distribution network is screened and counted, as well as the number of abnormal nodes δ in each region of the distribution network are screened and counted. q , and then calculate the total number of nodes in each area of ​​the distribution network δ q ′, analyze the abnormal node density index K in each area of ​​the distribution network q =K′ q ·τ1+K′ q ′·τ2, where K′ q represents the proportion index of abnormal nodes in the qth area of ​​the distribution network, K′ q ′ represents the density of abnormal nodes in the qth area of ​​the distribution network, τ1 represents the weight factor of the distribution network abnormal node ratio index, τ2 represents the weight factor of the distribution network abnormal node density, and τ1+τ2=1, where D represents the total number of distances from the target point to different reference points.

9. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1 is characterized by: The specific formula for analyzing the abnormality index of each area of ​​the distribution network is: q =K q β1+Ρ q β2, where K q represents the abnormal node density index of the qth area of ​​the distribution network, P q represents the abnormal circuit density index of the qth area of ​​the distribution network, β1 represents the weight factor of the set abnormal node density index, β2 represents the weight factor of the set abnormal circuit density index, and β1+β2=1.

10. The data visualization analysis system for a power distribution system based on source, grid, load and storage according to claim 1, characterized in that: The step of ranking the areas of the abnormal overall regions of the distribution network includes: Compare the abnormality index of each area of ​​the distribution network with the preset abnormality threshold. If the abnormality index of a certain area of ​​the distribution network is greater than the preset abnormality threshold, the area of ​​the distribution network is considered an abnormal area, and the abnormal areas of the distribution network are screened out. The simulation software of the distribution network is used to obtain the distances between the center points of each abnormal area of ​​the distribution network and its corresponding adjacent abnormal areas. The topological map of the distribution network is then used to obtain the distances between the center points of each abnormal area of ​​the distribution network and its corresponding adjacent abnormal areas. The adjacent abnormal areas whose corresponding center point distances of each abnormal area of ​​the distribution network are less than or equal to the preset adjacent distance are counted, and the adjacent abnormal areas are merged with the corresponding abnormal areas into abnormal overall areas, thereby obtaining the abnormal overall areas of the distribution network. The areas of the abnormal overall areas of the distribution network are obtained through the simulation software of the distribution network, and the abnormal overall areas of the distribution network are sorted according to their area sizes.

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