A method and system for determining substation information

Abstract

The present invention provides a method and system for determining substation information, which relates to the technical field of substations. The method includes constructing a cell graph structure; processing the cell graph structure based on a graph neural network model to determine the target cell where the substation is located; using a monitoring processing model to determine multiple preliminary locations with sparse traffic; inputting the power load information, location information, building layout information, road layout information, and multiple preliminary locations with sparse traffic of each cell to be powered into a power transmission information determination model to determine the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic; determining the target installation location of the substation based on the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic. This method can quickly and accurately determine the installation location of the substation.

Description

Technical Field

[0001] The present invention relates to the technical field of substations, and in particular to a method and system for determining substation information. Background Art

[0002] In the power supply sector, substation location selection is a critical decision. A suitable substation location not only affects the efficient supply of electricity but also directly impacts power loss, equipment investment, operating costs, and other factors. With the accelerating pace of urbanization and the continuous growth of electricity demand, efficiently and accurately determining the geographic location of substations has become a pressing issue in the power sector. Traditional substation site selection methods rely primarily on manual evaluation and empirical judgment. This approach is inefficient, highly subjective, and fails to fully consider multiple factors.

[0003] Therefore, how to quickly and accurately determine the installation location of the substation is a problem that needs to be solved urgently. Summary of the Invention

[0004] The main technical problem solved by the present invention is how to quickly and accurately determine the installation location of a transformer substation.

[0005] According to a first aspect, the present invention provides a method for determining substation information, comprising: obtaining information of multiple cells to be powered, wherein the information of the multiple cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; constructing a cell graph structure, wherein the cell graph structure includes multiple nodes and multiple edges between the multiple nodes, each of the multiple nodes represents a cell to be powered, and the node characteristics of each node include power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; processing the cell graph structure based on a graph neural network model to determine the target cell where the substation is located; obtaining a monitoring video of the target cell where the substation is located, The building layout information and road layout information of the target cell; based on the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell, a monitoring processing model is used to determine multiple preliminary locations with sparse traffic; the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary locations with sparse traffic are input into the power transmission information determination model to determine the power transmission loss degree of each preliminary location with sparse traffic and the total length of the transmission line of each preliminary location with sparse traffic; based on the power transmission loss degree of each preliminary location with sparse traffic and the total length of the transmission line of each preliminary location with sparse traffic, the target installation location of the substation is determined.

[0006] Furthermore, the input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target cell where the substation is located, and the multiple edge features of each of the multiple edges include the distance and direction between the cells.

[0007] Furthermore, the monitoring and processing model is a gated loop unit, the input of which is the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell, and the output of the monitoring and processing model is a plurality of preliminary locations with sparse traffic.

[0008] Furthermore, the method of determining the target installation location of the substation based on the degree of power transmission loss at each of the preliminary locations with sparse traffic and the total length of the transmission lines at each of the preliminary locations with sparse traffic includes: assigning different weights to the degree of power transmission loss at each of the preliminary locations with sparse traffic and the total length of the transmission lines at each of the preliminary locations with sparse traffic, respectively, and then performing weighted summation to obtain the power cost coefficient of each of the preliminary locations with sparse traffic, and taking the installation location of the substation with the lowest power cost coefficient as the target installation location of the substation.

[0009] Furthermore, the power transmission information determination model is a deep neural network model, and the input of the power transmission information determination model is the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary locations with sparse traffic; the output of the power transmission information determination model is the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic.

[0010] According to a second aspect, the present invention provides a system for determining substation information, comprising: a first acquisition module, configured to acquire information of a plurality of cells to be powered, wherein the information of the plurality of cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered;

[0011] A construction module is used to construct a cell graph structure, wherein the cell graph structure includes multiple nodes and multiple edges between the multiple nodes, each of the multiple nodes represents a cell to be powered, and the node characteristics of each node include power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered;

[0012] A target cell determination module is used to process the cell graph structure based on a graph neural network model to determine the target cell where the substation is located;

[0013] The second acquisition module is used to obtain the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell;

[0014] a monitoring processing module, configured to determine a plurality of preliminary locations with sparse pedestrian flow using a monitoring processing model based on a monitoring video of a target cell where the substation is located, building layout information of the target cell, and road layout information of the target cell;

[0015] A first acquisition module is configured to input the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary selected locations with sparse pedestrian traffic into a power transmission information determination model to determine the degree of power transmission loss at each preliminary selected location with sparse pedestrian traffic and the total length of the transmission line at each preliminary selected location with sparse pedestrian traffic;

[0016] The installation location determination module is used to determine the target installation location of the substation based on the power transmission loss level of each preliminary location with sparse traffic and the total length of the transmission line of each preliminary location with sparse traffic.

[0017] Furthermore, the input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target cell where the substation is located, and the multiple edge features of each of the multiple edges include the distance and direction between the cells.

[0018] Furthermore, the monitoring and processing model is a gated loop unit, the input of which is the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell, and the output of the monitoring and processing model is a plurality of preliminary locations with sparse traffic.

[0019] Furthermore, the installation location determination module is also used to: assign different weights to the degree of power transmission loss of each preliminary location with sparse traffic and the total length of the transmission line of each preliminary location with sparse traffic, and then perform weighted summation to obtain the power cost coefficient of each preliminary location with sparse traffic, and take the installation location of the substation with the lowest power cost coefficient as the target installation location of the substation.

[0020] Furthermore, the power transmission information determination model is a deep neural network model, and the input of the power transmission information determination model is the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary locations with sparse traffic; the output of the power transmission information determination model is the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic.

[0021] The present invention provides a method and system for determining substation information, the method comprising obtaining information of a plurality of cells to be powered, wherein the information of the plurality of cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; constructing a cell graph structure, wherein the cell graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, each of the plurality of nodes represents a cell to be powered, and the node features of each node include power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; processing the cell graph structure based on a graph neural network model to determine a target cell where the substation is located; obtaining a monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell. information, and road layout information of the target cell; determine multiple preliminary locations with sparse traffic using a monitoring processing model based on the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell; input the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary locations with sparse traffic into the power transmission information determination model to determine the power transmission loss degree of each preliminary location with sparse traffic and the total length of the transmission line of each preliminary location with sparse traffic; determine the target installation location of the substation based on the power transmission loss degree of each preliminary location with sparse traffic and the total length of the transmission line of each preliminary location with sparse traffic. This method can quickly and accurately determine the installation location of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a method for determining substation information provided by an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of a system for determining substation information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In an embodiment of the present invention, there is provided Figure 1 A method for determining substation information is shown, and the method for determining substation information includes steps S1 to S7:

[0025] Step S1, obtaining information of multiple cells to be powered, wherein the information of multiple cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered.

[0026] In some embodiments, the power load information of each cell to be powered includes power consumption, load curve, power load distribution in different time periods, power load distribution in different locations, load types of different devices in the cell, etc.

[0027] In some embodiments, the location information of each cell to be powered may be represented as geographical location coordinate information of each cell to be powered in the city, including longitude and latitude.

[0028] The building layout information includes the distribution, type, volume ratio and other information of the buildings in each community to be powered.

[0029] The road layout information includes the layout, width, connection status and other information of the roads in each community to be powered.

[0030] In some embodiments, the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, and the road layout information of each cell to be powered can be obtained through planning drawings, road planning maps, etc. provided by power companies, geographic information systems, and urban planning departments.

[0031] Step S2: construct a cell graph structure, which includes multiple nodes and multiple edges between the multiple nodes. Each of the multiple nodes represents a cell to be powered, and the node characteristics of each node include the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, and the road layout information of each cell to be powered.

[0032] The cell graph structure refers to a graph structure that treats each cell to be powered as a node and uses edges between nodes to represent the relationship between cells.

[0033] A graph structure consists of multiple nodes and multiple edges. A graph structure is a data structure composed of two parts: nodes and edges.

[0034] Step S3: Process the cell graph structure based on the graph neural network model to determine the target cell where the substation is located.

[0035] The input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target cell where the substation is located. Multiple edge features for each of the multiple edges include the distance and direction between cells. The graph neural network model includes a graph neural network (GNN) and a fully connected layer. A GNN is a type of neural network that directly operates on graph-structured data.

[0036] In power supply network planning, the spatial relationship between cells is a very important factor. By building a graph structure, spatial information such as the distance and direction between cells can be encoded into edge features, thereby better considering the spatial relationship between cells.

[0037] The target cell where the substation is located is a cell selected by the graph neural network model from multiple cells to be powered, taking into account the power supply demand and resource allocation of the entire area. The target cell where the substation is located is one of the target cells where the substation is located. Power supply network planning usually needs to consider the power supply demand and resource allocation of the entire area. The use of the graph neural network model can process the cell graph structure on a global scale, comprehensively consider the interactions between the nodes of each cell, and comprehensively consider the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, and the road layout information of each cell to be powered, thereby achieving global optimization and ultimately determining the target cell where the substation is located.

[0038] In the specific application scenario of determining the target cell where the substation is located, constructing a cell graph structure and using a graph neural network model can fully consider spatial relationships, achieve global optimization, and adaptively learn feature representations, thereby accurately determining the target cell where the substation is located.

[0039] Step S4: Obtain the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell.

[0040] The surveillance video of the target cell where the substation is located is a surveillance video obtained by monitoring the target cell. In some embodiments, the surveillance video of the target cell where the substation is located can be obtained by synthesizing the surveillance videos from multiple cameras in the target cell. In some embodiments, the surveillance video of the target cell where the substation is located can be obtained by splicing the surveillance videos from multiple cameras in the target cell. In some embodiments, the surveillance video of the target cell where the substation is located can be obtained by superimposing the surveillance videos from multiple cameras in the target cell using a video fusion algorithm.

[0041] Step S5: determining a plurality of preliminary locations with sparse pedestrian flow using a monitoring processing model based on the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell.

[0042] The monitoring processing model is a gated recurrent unit. Its inputs include surveillance video of the target cell where the substation is located, building layout information of the target cell, and road layout information of the target cell. Its outputs are multiple pre-selected locations with low pedestrian traffic. In some embodiments, the monitoring processing model is trained using a gradient descent method.

[0043] The multiple preliminary selected locations with sparse crowd flow are preliminary selected locations output by the gated recurrent unit, where the crowd flow is relatively sparse and the locations are relatively reasonable within the target cell.

[0044] The Gated Recurrent Unit (GRU) is used to process sequence data and time series information. It consists of three components: a memory unit, an update gate, and a reset gate. The GRU model can process surveillance videos of the target cell where the substation is located over consecutive time periods. This allows for better capture of relationships within the time series of surveillance videos of the target cell where the substation is located. It can also output features that comprehensively consider the correlations between surveillance videos of the target cell where the substation is located at each point in time, making the output features more accurate and comprehensive.

[0045] The gated circulation unit extracts the trajectory and trend of personnel flow by analyzing surveillance videos, building layout information and road layout information, divides the personnel flow in the target community where the substation is located into different time periods or spatial areas, and then analyzes the density and distribution of personnel flow in each time period or spatial area, and finds the preliminary locations where the personnel flow is relatively sparse and the installation location is relatively reasonable in the community, which are used as multiple preliminary locations with sparse personnel flow.

[0046] In some embodiments, the monitoring processing model includes a crowd distribution determination layer, a regional preliminary screening layer, and a preliminary location determination layer. The input of the crowd distribution determination layer is the monitoring video of the target cell where the substation is located, the output of the crowd distribution determination layer is the crowd distribution map of the target cell where the substation is located, the input of the regional preliminary screening layer is the crowd distribution map of the target cell where the substation is located, the output of the regional preliminary screening layer is a plurality of preliminary screening areas with sparse crowds, the input of the preliminary location determination layer is a plurality of preliminary screening areas with sparse crowds, the building layout information of the target cell, and the road layout information of the target cell, and the output of the preliminary location determination layer is a plurality of preliminary locations with sparse crowds. The plurality of preliminary screening areas with sparse crowds are areas with less crowds that are output only by the regional preliminary screening layer, and the plurality of preliminary locations with sparse crowds are preliminary locations with relatively sparse crowds and relatively reasonable positions within the cell, which are obtained by the comprehensive output of the preliminary location determination layer based on the plurality of preliminary screening areas with sparse crowds, the building layout information of the target cell, and the road layout information of the target cell. The crowd flow distribution determination layer extracts crowd flow information from surveillance video. The regional preliminary screening layer performs preliminary screening of crowd flow distribution. The preliminary location determination layer further considers multiple factors to determine the final location. Each layer has clear responsibilities and goals, avoiding task confusion and information redundancy. The output of each layer serves as input to the next layer, forming an end-to-end data processing flow that facilitates information transmission and processing. This layered design allows for efficient data organization and utilization, improving the overall performance and expressiveness of the system.

[0047] Step S6: Input the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary locations with sparse traffic into the power transmission information determination model to determine the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic.

[0048] The power transmission information determination model is a deep neural network model. The input of the power transmission information determination model is the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary locations with sparse traffic. The output of the power transmission information determination model is the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic.

[0049] During power transmission, factors such as the impedance, resistance, and inductance of wires can cause power loss, impacting the quality and stability of power supply. The power transmission loss level for each sparsely populated primary location refers to the power loss experienced when transmitting power from each sparsely populated primary location to all cells to be powered.

[0050] Deep neural network models include deep neural networks (DNNs). DNNs can include multiple processing layers, each composed of multiple neurons, and each neuron performs matrix transformations on data. The parameters used in the matrix can be obtained through training. By training the DNN, the model can learn the complex relationships between various features from multiple inputs, including the power load information, location information, building layout information, road layout information, and preliminary location information of the cell to be powered, thereby accurately predicting the degree of transmission loss and transmission line length. DNNs enable end-to-end learning and can better utilize input information, including power load, location information, building layout information, road layout information, and preliminary location information, enabling the model to fully consider the impact of this information on power loss and transmission line length.

[0051] Step S7 : determining a target installation location of the substation based on the degree of power transmission loss at each of the preliminary selected locations with sparse traffic and the total length of the power transmission line at each of the preliminary selected locations with sparse traffic.

[0052] In some embodiments, determining the target installation location of the substation based on the degree of power transmission loss at each of the preliminary locations with sparse traffic and the total length of the transmission lines at each of the preliminary locations with sparse traffic includes: assigning different weights to the degree of power transmission loss at each of the preliminary locations with sparse traffic and the total length of the transmission lines at each of the preliminary locations with sparse traffic, respectively, and then performing weighted summation to obtain the power cost coefficient of each preliminary location with sparse traffic, and taking the installation location of the substation with the lowest power cost coefficient as the target installation location of the substation.

[0053] By assigning different weights to power transmission losses and total transmission line length, and taking these two factors into account, an electricity cost coefficient is derived. By comparing the power cost coefficients of different substation locations, the substation with the lowest power cost coefficient can be selected as the target installation location. This approach minimizes power transmission losses and transmission line length, thereby reducing energy waste and improving power supply efficiency and reliability.

[0054] The electricity cost coefficient is used to evaluate the electricity cost at different substation locations.

[0055] Based on the same inventive concept, Figure 2A schematic diagram of a system for determining substation information provided by an embodiment of the present invention, the system for determining substation information includes:

[0056] A first acquisition module 21 is configured to acquire information of a plurality of cells to be powered, wherein the information of the plurality of cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered;

[0057] A construction module 22 is configured to construct a cell graph structure, wherein the cell graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein each of the plurality of nodes represents a cell to be powered, and the node characteristics of each node include power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered;

[0058] A target cell determination module 23 is configured to process the cell graph structure based on a graph neural network model to determine the target cell where the substation is located;

[0059] The second acquisition module 24 is used to obtain the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell;

[0060] A monitoring processing module 25 is configured to determine a plurality of preliminary locations with sparse pedestrian flow using a monitoring processing model based on a monitoring video of a target cell where the substation is located, building layout information of the target cell, and road layout information of the target cell;

[0061] The power transmission information determination module 26 is configured to input the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and the multiple preliminary selected locations with sparse pedestrian traffic into the power transmission information determination model to determine the power transmission loss degree of each preliminary selected location with sparse pedestrian traffic and the total length of the transmission line of each preliminary selected location with sparse pedestrian traffic;

[0062] The installation location determination module 27 is configured to determine a target installation location of the substation based on the degree of power transmission loss at each of the preliminary selected locations with sparse traffic and the total length of the power transmission line at each of the preliminary selected locations with sparse traffic.

Claims

1. A method for determining substation information, characterized in that: include: Acquire information of a plurality of cells to be powered, wherein the information of the plurality of cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; Constructing a cell graph structure, the cell graph structure including a plurality of nodes and a plurality of edges between the plurality of nodes, each of the plurality of nodes representing a cell to be powered, and node features of each node including power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; The cell graph structure is processed based on a graph neural network model to determine the target cell where the substation is located. The input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target cell where the substation is located. The multiple edge features of each of the multiple edges include the distance and direction between the cells. Obtain surveillance video of the target community where the substation is located, building layout information of the target community, and road layout information of the target community; A monitoring processing model is used to determine multiple preliminary locations with sparse pedestrian traffic. The monitoring processing model is a gated recurrent unit. The input of the monitoring processing model is a surveillance video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell. The output of the monitoring processing model is multiple preliminary locations with sparse pedestrian traffic. Determine the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic based on a power transmission information determination model. The power transmission information determination model is a deep neural network model. The input of the power transmission information determination model is the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and multiple preliminary locations with sparse traffic. The output of the power transmission information determination model is the degree of power transmission loss at each preliminary location with sparse traffic and the total length of the transmission line at each preliminary location with sparse traffic; After assigning different weights to the degree of power transmission loss and the total length of the transmission line at each preliminary location with sparse traffic, the power cost coefficient of each preliminary location with sparse traffic is obtained by weighted summation. The installation location of the substation with the lowest power cost coefficient is taken as the target installation location of the substation.

2. A system for determining substation information, characterized in that: include: A first acquisition module is used to acquire information of multiple cells to be powered, wherein the information of the multiple cells to be powered includes power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; A construction module is used to construct a cell graph structure, wherein the cell graph structure includes multiple nodes and multiple edges between the multiple nodes, each of the multiple nodes represents a cell to be powered, and the node characteristics of each node include power load information of each cell to be powered, location information of each cell to be powered, building layout information of each cell to be powered, and road layout information of each cell to be powered; A target cell determination module is configured to process the cell graph structure based on a graph neural network model to determine the target cell where the substation is located. The input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target cell where the substation is located. The multiple edge features of each of the multiple edges include the distance and direction between cells. The second acquisition module is used to obtain the monitoring video of the target cell where the substation is located, the building layout information of the target cell, and the road layout information of the target cell; a monitoring and processing module for determining a plurality of preliminary locations with sparse crowds using a monitoring and processing model, the monitoring and processing model being a gated loop unit, the input of which is a monitoring video of a target cell where the substation is located, information about the building layout of the target cell, and information about the road layout of the target cell, and the output of which is a plurality of preliminary locations with sparse crowds; a power transmission information determination module, configured to determine, based on a power transmission information determination model, the degree of power transmission loss at each sparsely populated preliminary location and the total length of the transmission line at each sparsely populated preliminary location, the power transmission information determination model being a deep neural network model, the inputs of which being the power load information of each cell to be powered, the location information of each cell to be powered, the building layout information of each cell to be powered, the road layout information of each cell to be powered, and multiple sparsely populated preliminary locations, and the outputs of the power transmission information determination model being the degree of power transmission loss at each sparsely populated preliminary location and the total length of the transmission line at each sparsely populated preliminary location; The installation location determination module is used to assign different weights to the degree of power transmission loss and the total length of the transmission line at each preliminary location with sparse traffic, and then obtain the power cost coefficient of each preliminary location with sparse traffic after weighted summation. The installation location of the substation with the lowest power cost coefficient is used as the target installation location of the substation.

Images