Station line transformer user penetration dynamic rendering method based on energy flow diagram

Abstract

The invention belongs to the technical field of power system visualization, and particularly relates to a station line transformer user penetration dynamic rendering method based on an energy flow diagram. According to the method, firstly, edge computing nodes are arranged at station line transformer user key points, time sequence data compression is improved, redundant transmission is reduced, and real-time accurate data are provided for dynamic rendering; and then an improved layered topology modeling algorithm is provided, and the power flow is accurately analyzed and predicted, so that the rendering basis of the energy flow graph is more scientific. And then a local energy flow visualization layer is generated according to user interaction requirements, so that the calculation pressure is reduced, and the interaction response speed and the energy flow graph display fluency are improved. And finally, realizing smooth dynamic rendering of the two-dimensional energy flow diagram and the three-dimensional energy flow diagram based on WebGPU and Shader optimization. According to the method, the calculation pressure is greatly reduced, rapid interaction is realized, and the dynamically changing local energy flow diagram can be smoothly displayed.

Description

Technical Field

[0001] The invention belongs to the technical field of power system visualization, and in particular relates to a station-line-transformer-user penetration dynamic rendering method based on an energy flow diagram. Background Art

[0002] In order to meet the user's demand for rapid interaction with detailed energy flow information in a local area and reduce the visualization computing pressure of the entire network, the present invention dynamically generates a local energy flow data visualization layer according to the user's interaction needs. In today's era, the power system continues to develop rapidly, and the complexity of the power grid structure shows an exponential growth trend. This change makes the visualization analysis of the power grid operation status an indispensable and important part of the power industry, and the dynamic rendering of the energy flow diagram is the core technical requirement.

[0003] The existing dynamic rendering methods of energy flow diagrams have exposed many insurmountable defects in practical applications. In the initial stage of data collection, due to the limitations of technical means, it is impossible to efficiently carry out real-time data collection and processing. There is often a significant lag in data acquisition, which makes the energy flow diagram extremely slow in dynamic update and unable to accurately and timely reflect the ever-changing real-time status of the power grid. The power grid topology modeling link is also unsatisfactory, lacking an effective strategy that adapts to the characteristics of the power grid hierarchy. This shortcoming directly leads to the difficulty in accurately analyzing the power flow, which makes it impossible for the energy flow diagram to accurately present the dynamic distribution and flow trend of power in various parts of the power grid during the dynamic rendering process, and cannot provide reliable decision-making basis for operation and maintenance personnel. In the process of visual calculation, the problem is also quite serious. Due to the lack of effective optimization of the calculation method, when faced with massive power grid data, the pressure of visual calculation of the entire network is like a mountain. When performing dynamic display of two-dimensional and three-dimensional energy flow diagrams, freezes and frame drops occur frequently, just like the picture is constantly "stuck" during playback. This not only seriously interferes with the user's dynamic perception of the power grid energy flow information, making it impossible for them to clearly and coherently grasp the energy flow changes, but also greatly hinders the user's ability to conduct in-depth analysis and accurate judgment on the power grid operation status. Summary of the invention

[0004] In view of the above-mentioned technical problems, the present invention proposes a station-line change user penetration dynamic rendering method based on energy flow diagram.

[0005] In order to achieve the above object, the technical solution adopted by the present invention comprises the following steps:

[0006] S1. First, edge computing nodes are deployed at key points of station line change to achieve real-time data collection and distributed processing. The distributed processing improves time series data compression and reduces redundant data transmission;

[0007] S2. An improved hierarchical topology modeling algorithm is proposed to decompose the power grid topology by level, and introduce time-series-based energy flow prediction to support the combination of real-time data and prediction data;

[0008] S3, then dynamically generate a visualization layer of local energy flow data according to user interaction requirements to reduce the visualization computing pressure of the entire network. The specific implementation is as follows:

[0009] S31. First, based on the hierarchical topology of the power grid G ​​= (N, ε, W), the minimum subgraph G of the user-specified area is calculated. sub , concentrates the whole network computing to the local area of ​​interest to the user and dynamically adjusts it according to real-time interaction;

[0010] S32, then according to the extracted subgraph G sub Dynamically generate a local energy flow visualization layer for each edge e ij ∈ε sub , ε sub is the subgraph G sub The edge set, calculate its energy flow intensity S ij : Where P ij is the power flow from node i to node j, W ij For edge e ij The power transmission capacity of the energy flow intensity is used to dynamically adjust the thickness and color of the edges in the layer; for each node i∈N sub , N sub Subgraph G sub The node set of Formula to aggregate, where S i Indicates the node load ratio, mapped to the node color and icon size; based on S ij and S i As a result, only key edges and nodes are drawn to reduce rendering complexity;

[0011] S33. Finally, the improved intelligent region perception algorithm is used to ensure the introduction of region perception weight ω in real-time interaction. i , only process the region of interest N sub The data in the table is changed when the user interaction changes, only the newly added and reduced node sets ΔN sub Update the energy flow calculation and cache the generated local layer data. When users make repeated queries, they can directly call the cached results to reduce the cost of multiple calculations.

[0012] S4. Finally, dynamic rendering based on WebGPU and Shader optimization is used to achieve smooth display of two-dimensional and three-dimensional energy flow diagrams.

[0013] Preferably, the distributed processing in step S1 to improve the compression of time series data is implemented by performing the time series data {(x1, y1), (x2, y2), ... (x n ,y n )}Use the least squares method to fit the straight line y = αx + β, where The fitting error calculation formula is: where x i is the time point, y i is the data value corresponding to the time point, n is the number of samples in the current segment, and when the error is less than the error tolerance threshold, the segment data is compressed into the starting point, end point and fitting parameters.

[0014] Preferably, the implementation steps of the hierarchical topology modeling algorithm in step S2 are:

[0015] S21. Divide the grid topology into four layers: station, line, transformer, and household, forming a multi-layer topological structure based on a graph. The node set is N = {N1, N2, N3, N4}, where N 站 , N 线 ,N 变 ,N 户 They are substation nodes, transmission station nodes, transformer nodes and user nodes, ε={e ij |i∈N l ,j∈N l+1 ,l=1,2,3}where e ij To represent the connection relationship between node i in level l and node j in level l+1, a weighted directed graph G = (N, ε, W) is used, where the weight matrix W represents the power transmission capacity of the line;

[0016] S22. In the hierarchical topology, the power flow follows the physical laws of the power grid, and each node satisfies the power balance equation: P in,i =P out,i +P loss,i , where P in,i is the input power of node i, P out,i is the output power of node i, P loss,i is the power loss, which can be further defined as: Where P ij is the power from node i to j, R ij is the resistance of the line, V i is the voltage of node i, calculates the power transfer relationship of each layer of nodes, and recursively updates the power flow state of the entire network;

[0017] S23, then use LSTM to predict the power of node i, and use the inter-layer coupling constraint to optimize the prediction results so that the prediction results of the upper and lower layers are consistent. The objective function is: in is the predicted future power value of node i, l is the number of layers, is the total predicted input power of all relevant nodes in the next layer;

[0018] S24. To solve the problem of differences between real-time data and predicted data, a dynamic data fusion method is used, and the fusion power value is: Where P real,i,t+1 It is real-time data. is the predicted data; real,i ,φ pred,i They are the credibility of real-time data and predicted data respectively.

[0019] Preferably, the constraint condition of the objective function in step S23 is: Ensure that the input power of each node is equal to the sum of the output power and the loss power, The output power of each node cannot exceed its design capacity P capacity,i .

[0020] Compared with the prior art, the advantages and positive effects of the present invention are that, through edge computing nodes and optimized time series data compression, a more timely and streamlined data foundation is provided for dynamic rendering, ensuring that the rendered content can reflect the latest status of the power grid and that data transmission is efficient. The improved hierarchical topology modeling algorithm incorporates energy flow prediction, making the rendering basis more scientific and accurate, and better presenting the power flow trend. When generating visualization layers, it focuses on the user's area of ​​interest, optimizes calculations and caches data, greatly reduces computing pressure, achieves fast interaction, and can smoothly display dynamically changing local energy flow diagrams. DETAILED DESCRIPTION

[0021] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0023] Embodiment: With the development of the power system, the structure of the power grid is becoming increasingly complex, and the demand for visual analysis of the power grid operation status is growing. In particular, the dynamic rendering of the energy flow diagram has become a key technical requirement. There are many defects in the traditional dynamic rendering method of the energy flow diagram. In the data collection link, it is impossible to achieve efficient real-time data collection and processing, resulting in slow dynamic updates of the energy flow diagram and failure to timely reflect the real-time status changes of the power grid. The power grid topology modeling lacks adaptability and it is difficult to accurately analyze the power flow, so that the energy flow diagram cannot accurately present the dynamic distribution and flow trend of power during dynamic rendering. In the process of visual calculation, due to the failure to effectively optimize the calculation method, facing massive data, the pressure of visual calculation of the entire network is huge. When performing dynamic display of two-dimensional and three-dimensional energy flow diagrams, freezes and frame drops frequently occur, which seriously affects the user's dynamic perception and analysis and judgment of the power grid energy flow information.

[0024] In order to achieve efficient collection and processing of station-line changer data in the power system and reduce data transmission redundancy, the present invention adopts a solution of arranging edge computing nodes at key points of station-line changer and improving time series data compression. In traditional power grid data processing, data collection efficiency is low and transmission redundancy is serious. The present invention arranges edge computing nodes at key points of station-line changer to achieve real-time data collection and distributed processing. For time series data {(x1, y1), (x2, y2), ... (x n ,y n )}Use the least squares method to fit the straight line y = αx + β, where The fitting error calculation formula is: where x i is the time point, y i is the data value corresponding to the time point, n is the number of samples in the current segment, and when the error is less than the error tolerance threshold, the segment data is compressed into the starting point, end point and fitting parameters. In this way, a large amount of similar or slowly changing data can be effectively compressed, reducing the amount of data that needs to be transmitted and improving the real-time performance of data collection.

[0025] Next, considering that the existing power grid topology modeling lacks an effective hierarchical strategy and it is difficult to accurately analyze power flow, in order to accurately construct a power grid topology model and accurately analyze and predict power flow, the present invention proposes an improved hierarchical topology modeling algorithm. The power grid topology is divided into four layers: station, line, transformer, and household, forming a multi-layer topology structure based on a graph, and the node set is N = {N1, N2, N3, N4}, where N 站 , N 线 ,N 变 ,N 户 They are substation nodes, transmission station nodes, transformer nodes and user nodes, ε={e ij |i∈N l ,j∈N l+1 ,l=1,2,3}where eij The connection relationship between node i in level l and node j in level l+1 is represented by a weighted directed graph G=(N,ε,W), where the weight matrix W represents the power transmission capacity of the line; in the hierarchical topology, the power flow follows the physical laws of the power grid, and each node satisfies the power balance equation: P in,i =P out,i +P loss,i , where P in,i is the input power of node i, P out,i is the output power of node i, P loss,i is the power loss, which can be further defined as: Where P ij is the power from node i to j, R ij is the resistance of the line, V i is the voltage of node i, calculate the power transfer relationship of each layer of nodes, and recursively update the power flow state of the entire network; then use LSTM to predict the power of node i, and use the inter-layer coupling constraint to optimize the prediction result so that the prediction results of the upper and lower layers are consistent. The objective function is: in is the predicted future power value of node i, l is the number of layers, is the total predicted input power of all relevant nodes in the next layer, and the constraint of the objective function is Ensure that the input power of each node is equal to the sum of the output power and the loss power, The output power of each node cannot exceed its design capacity P capacity,i ; To solve the problem of differences between real-time data and predicted data, a dynamic data fusion method is used, and the fusion power value is: Where P real,i,t+1 It is real-time data. is the predicted data; real,i ,φ pred,i The reliability of real-time data and predicted data is respectively. Through this hierarchical topology modeling algorithm, when analyzing and predicting the power flow of a city power grid, it is possible to accurately simulate the power transfer and change trends between different levels and discover potential problems in advance.

[0026] Then, in order to meet the user's demand for fast interaction with detailed energy flow information in a local area and reduce the visualization calculation pressure of the entire network, the present invention dynamically generates a local energy flow data visualization layer according to the user's interaction needs. First, based on the hierarchical topology G = (N, ε, W) of the power grid, the minimum subgraph G of the user-specified area is calculated. sub , the whole network calculation is concentrated on the local area of ​​interest to the user, and dynamically adjusted according to the real-time interaction; then according to the extracted subgraph G sub Dynamically generate a local energy flow visualization layer for each edge e ij ∈εsub , ε sub is the subgraph G sub The edge set, calculate its energy flow intensity S ij : Where P ij is the power flow from node i to node j, W ij For edge e ij The power transmission capacity of the energy flow intensity is used to dynamically adjust the thickness and color of the edges in the layer; for each node i∈N sub , N sub Subgraph G sub The node set of Formula to aggregate, where S i Indicates the node load ratio, mapped to the node color and icon size; based on S ij and S i As a result, only key edges and nodes are drawn to reduce rendering complexity; finally, the improved intelligent region perception algorithm is used to ensure real-time interaction and introduce region perception weight ω i , only process the region of interest N sub The data in the table is changed when the user interaction changes, only the newly added and reduced node sets ΔN sub Update the energy flow calculation and cache the generated local layer data. When users query repeatedly, they can directly call the cached results to reduce the cost of multiple calculations. In this way, users can quickly obtain detailed energy flow information in local areas, the interactive response speed is significantly improved, and the display of two-dimensional and three-dimensional energy flow diagrams is smoother.

[0027] Finally, based on the dynamic rendering optimized by WebGPU and Shader, WebGPU makes full use of the parallel processing capabilities of modern graphics hardware to accelerate the visualization rendering of energy flow data. Shader optimizes the vertex shader, fragment shader, etc. in the graphics rendering pipeline, adjusts the calculation method of color and transparency according to the energy flow intensity, and reduces unnecessary computing overhead. When displaying two-dimensional and three-dimensional energy flow diagrams of large-scale power grids, it can effectively avoid freezes and delays and achieve smooth dynamic rendering effects.

[0028] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A station-line change-user penetration dynamic rendering method based on energy flow diagram, characterized in that: The following steps are involved: S1. First, edge computing nodes are deployed at key points of station line change to achieve real-time data collection and distributed processing. The distributed processing improves time series data compression and reduces redundant data transmission; S2. An improved hierarchical topology modeling algorithm is proposed to decompose the power grid topology by level, and introduce time-series-based energy flow prediction to support the combination of real-time data and prediction data; S3, then dynamically generate a visualization layer of local energy flow data according to user interaction requirements to reduce the visualization computing pressure of the entire network. The specific implementation is as follows: S31. First, based on the hierarchical topology of the power grid G ​​= (N, ε, W), the minimum subgraph G of the user-specified area is calculated. sub , concentrates the computing of the entire network to the local area of ​​interest to the user, and dynamically adjusts it according to real-time interaction; S32, then according to the extracted subgraph G sub Dynamically generate a local energy flow visualization layer for each edge e ij ∈ε sub , ε sub is the subgraph G sub The edge set, calculate its energy flow intensity S ij : Where P ij is the power flow from node i to node j, W ij For edge e ij The power transmission capacity of the energy flow intensity is used to dynamically adjust the thickness and color of the edges in the layer; for each node i∈N sub , N sub Subgraph G sub The node set of Formula to aggregate, where S i Indicates the node load ratio, mapped to the node color and icon size; based on S ij and S i As a result, only key edges and nodes are drawn to reduce rendering complexity; S33. Finally, the improved intelligent region perception algorithm is used to ensure the introduction of region perception weight ω in real-time interaction. i , only process the region of interest N sub The data in the table is changed when the user interaction changes, only the newly added and reduced node sets ΔN sub Update the energy flow calculation and cache the generated local layer data. When users make repeated queries, they can directly call the cached results to reduce the cost of multiple calculations. S4. Finally, dynamic rendering based on WebGPU and Shader optimization is used to achieve smooth display of two-dimensional and three-dimensional energy flow diagrams.

2. According to the method of claim 1, the station line change user penetration dynamic rendering method based on energy flow diagram is characterized in that: The implementation step of improving time series data compression through distributed processing in step S1 is to perform compression on the time series data {(x1, y1), (x2, y2), ... (x n ,y n )}Use the least squares method to fit the straight line y = αx + β, where The fitting error calculation formula is: where x i is the time point, y i is the data value corresponding to the time point, n is the number of samples in the current segment, and when the error is less than the error tolerance threshold, the segment data is compressed into the starting point, end point and fitting parameters.

3. According to the method of claim 1, the station-line change user penetration dynamic rendering method based on energy flow diagram is characterized in that: The implementation steps of the hierarchical topology modeling algorithm in step S2 are: S21. Divide the grid topology into four layers: station, line, transformer, and household, forming a multi-layer topological structure based on a graph. The node set is N = {N1, N2, N3, N4}, where N 站 , N 线 ,N 变 ,N 户 They are substation nodes, transmission station nodes, transformer nodes and user nodes, ε={e ij |i∈N l ,j∈N l+1 ,l=1,2,3}where e ij To represent the connection relationship between node i in level l and node j in level l+1, a weighted directed graph G = (N, ε, W) is used, where the weight matrix W represents the power transmission capacity of the line; S22. In the hierarchical topology, the power flow follows the physical laws of the power grid, and each node satisfies the power balance equation: P in,i =P out,i +P loss,i , where P in,i is the input power of node i, P out,i is the output power of node i, P loss,i is the power loss, which can be further defined as: Where P ij is the power from node i to j, R ij is the resistance of the line, V i is the voltage of node i, calculates the power transfer relationship of each layer of nodes, and recursively updates the power flow state of the entire network; S23, then use LSTM to predict the power of node i, and use the inter-layer coupling constraint to optimize the prediction results so that the prediction results of the upper and lower layers are consistent. The objective function is: in is the predicted future power value of node i, l is the number of layers, is the total predicted input power of all relevant nodes in the next layer; S24. To solve the problem of differences between real-time data and predicted data, a dynamic data fusion method is used, and the fusion power value is: Where P real,i,t+1 It is real-time data. is the predicted data; real,i ,φ pred,i They are the credibility of real-time data and predicted data respectively.

4. According to the method of claim 3, the station line change user penetration dynamic rendering method based on energy flow diagram is characterized in that: The constraint condition of the objective function in step S23 is Ensure that the input power of each node is equal to the sum of the output power and the loss power, The output power of each node cannot exceed its design capacity P capacity,i .

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