An Artificial Intelligence-Based Substation Site Selection Method and System

Abstract

The present application discloses a substation site selection method and system based on artificial intelligence. The method includes: performing grid division on a target area to obtain a number of grid units, and calculating the load density eigenvalue of each grid unit based on the expansion planning data of the target area; inputting the meteorological forecast data and load density eigenvalue of each grid unit into a load prediction model to obtain a load prediction data set; clustering the load prediction data set to obtain a number of clustering regions, and calculating the load density of each clustering region; constructing a Voronoi diagram of the target area based on the clustering regions and load density, and obtaining initial candidate points based on the Voronoi diagram; constructing a multi-objective optimization model for substation site selection, inputting the initial candidate points into the multi-objective optimization model for solution, and obtaining the substation site selection of the target area. The substation site selection method of the present application improves the power supply reliability of the substation.

Description

Technical Field

[0001] The present application relates to the technical field of power system planning, and in particular to a substation site selection method and system based on artificial intelligence. Background Art

[0002] In urban expansion areas, distribution network planning faces complex challenges in substation site selection.

[0003] Traditional substation site selection is mostly based on simple load statistics, focusing only on the average load size of the region. For example, in the planning of some small and medium-sized cities, the location of substations is often determined based solely on the total historical electricity consumption of the administrative region. This approach ignores the differences in industrial layout, uneven population density, and the impact of meteorological factors on load.

[0004] It can be seen that how to improve the existing substation site selection method to improve the power supply reliability of the substation has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0005] The present application provides a substation site selection method and system based on artificial intelligence to solve the technical problem of how to improve the existing substation site selection method and achieve the effect of improving the power supply reliability of the substation.

[0006] In order to solve the above technical problems, the embodiment of the present application provides a substation site selection method based on artificial intelligence, including:

[0007] Performing rasterization processing on the target area to obtain a number of grid cells, and calculating the load density characteristic value of each grid cell based on the acquired expansion planning data of the target area;

[0008] Inputting the obtained weather forecast data of each grid unit and the load density characteristic value into a pre-built load prediction model for prediction, thereby obtaining a load prediction data set for the target area;

[0009] Clustering the load forecasting data set to obtain a number of clustering regions, and calculating the load density of each clustering region;

[0010] Constructing a Voronoi diagram of the target area based on the cluster area and the load density, and obtaining initial candidate points for substation site selection in the target area based on the Voronoi diagram;

[0011] A multi-objective optimization model for substation site selection in the target area is constructed, and at least the initial candidate points are input into the multi-objective optimization model for solving to obtain the substation site selection in the target area.

[0012] As one of the preferred solutions, the expansion planning data includes industrial layout data, population density data, and economic indicator data;

[0013] Calculating the load density eigenvalue of each grid cell based on the obtained expansion planning data of the target area includes:

[0014] According to the obtained industrial layout data, population density data, and economic indicator data of each grid cell, calculate the load density eigenvalue corresponding to each grid cell, expressed as:

[0015]

[0016] Wherein, The load density eigenvalue of the nth grid cell, is the quantization value of the industrial layout data, is the weight of the industrial layout data, is the quantization value of the population density data, is the weight of the population density data, is the quantization value of the economic indicator data,

[0017] As one of the preferred solutions, before inputting the obtained weather forecast data and load density eigenvalues of each grid cell into the pre-constructed load prediction model for prediction, it further includes:

[0018] Construct an initial load prediction model based on LSTM;

[0019] Train the initial load prediction model based on the obtained historical weather data, historical load data, and historical load density eigenvalue data. During the training process, calculate the loss function according to the load prediction value obtained through forward propagation and the historical load data, and update the parameters of the initial load prediction model according to the value of the loss function to obtain a trained load prediction model.

[0020] As one of the preferred solutions, clustering the load prediction data set to obtain a certain number of clustering regions includes:

[0021] Cluster the load prediction data set based on the improved K-means algorithm, which includes randomly selecting several sample data in the load prediction data set as clustering centers;

[0022] During the calculation process, calculate the distance from each sample data in the load prediction dataset to the cluster center, and divide the sample data into the cluster where the nearest cluster center is located according to the calculation result, and recalculate the mean value of the sample data in each cluster as the new cluster center, where the distance includes the spatial distance and the feature distance;

[0023] Repeat the above calculation process until the cluster center no longer changes, and obtain a certain number of initial cluster regions;

[0024] Calculate the silhouette coefficient of each sample data, evaluate the clustering quality of the initial cluster regions according to the silhouette coefficient, perform a secondary division on the initial cluster regions according to the result of the clustering quality evaluation, and obtain the cluster regions according to the result of the secondary division.

[0025] As one of the preferred solutions, the calculation of the load density of each cluster region includes:

[0026] Obtain the total load and the area of the region of all grid cells within the cluster region;

[0027] Calculate the initial load density of the cluster region according to the total load and the area of the region;

[0028] Introduce a load distribution non-uniformity index to correct the initial load density to obtain the load density of the cluster region, where the non-uniformity index is the standard deviation or coefficient of variation of the load within the region.

[0029] As one of the preferred solutions, the construction of the Voronoi diagram of the target region based on the cluster region and the load density includes:

[0030] Use the centroid of the cluster region as the initial seed point to construct an initial Voronoi diagram;

[0031] Adjust the boundary surface of the initial Voronoi diagram according to the load density, and the adjustment is designed to make the boundary surface shift towards the cluster region with a lower load density;

[0032] Optimize the position of the initial seed points in the adjusted initial Voronoi diagram based on the genetic algorithm to obtain the Voronoi diagram of the target region, and the optimization is designed to balance the load intensity of each Voronoi polygon.

[0033] As one of the preferred solutions, obtaining the initial candidate points for the substation location in the target region based on the Voronoi diagram includes:

[0034] Calculate the deviation rate of the area of each Voronoi polygon in the Voronoi diagram from the average area;

[0035] If the deviation rate exceeds the preset deviation rate range, re-optimize the position of the initial seed points based on the genetic algorithm; if the deviation rate is within the deviation rate range, use the optimized position of the seed points as the initial candidate points.

[0036] As one of the preferred solutions, the multi-objective optimization model for the substation site selection in the target area is constructed and expressed as:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] Among them, are the weight coefficients, is the maximum allowable value of electromagnetic radiation, is the maximum allowable value of noise, is the sum of the absolute differences between the actual power flow and the ideal power flow, is the total cost of substation construction, is the total operating cost of the substation, is the average power outage time, is the average power outage frequency, is the total length of all transmission lines between substations, is the comprehensive optimization objective, is the economic objective, is the reliability objective, is the environmental impact objective, is the power grid structure objective.

[0047] As one of the preferred solutions, at least input the initial candidate points into the multi-objective optimization model for solution to obtain the substation site selection in the target area, including:

[0048] Taking the initial candidate points as the initial population individuals, input them into the multi-objective optimization model for solution based on the improved genetic algorithm to obtain a candidate solution set; wherein the candidate solution set corresponds to the set of position coordinates of the candidate substations.

[0049] Sort the candidate solution set based on the TOPSIS method, and perform screening according to the sorting result to obtain the substation site selection in the target area.

[0050] Another embodiment of the present application provides an artificial intelligence-based substation site selection system, including:

[0051] A calculation module, configured to perform rasterization processing on the target area to obtain a certain number of raster units, and calculate the load density characteristic value of each raster unit based on the obtained expansion planning data of the target area.

[0052] A prediction module, configured to input the obtained weather forecast data and the load density characteristic values of each raster unit into a pre-constructed load prediction model for prediction to obtain a load prediction data set of the target area.

[0053] A clustering module, configured to cluster the load prediction data set to obtain a certain number of clustering regions, and calculate the load density of each clustering region.

[0054] A construction module, configured to construct a Voronoi diagram of the target area based on the clustering regions and the load density, and obtain the initial candidate points for the substation site selection in the target area based on the Voronoi diagram.

[0055] A generation module, configured to construct a multi-objective optimization model for the substation site selection in the target area, and input at least the initial candidate points into the multi-objective optimization model for solution to obtain the substation site selection in the target area.

[0056] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0057] (1) In the present application, by clustering the load prediction data set, different clustering regions are divided, and the load density of each region is calculated. On this basis, a Voronoi diagram is constructed. This diagram can naturally divide the target area into multiple sub-regions according to the load distribution characteristics, and the distance from the points in each sub-region to the nearest clustering center (i.e., the potential load concentration point) is the shortest. The initial candidate points obtained based on the Voronoi diagram are naturally distributed in and around the load concentration areas. Compared with randomly selecting candidate points, the search space in the subsequent optimization process is greatly reduced, the site selection efficiency is improved, and the substation position that meets the requirements can be found faster.

[0058] (2) The entire site selection process of this application is based on a large amount of data processing and analysis, from the expansion planning data of the target area, meteorological data to load forecasting data, etc., providing a solid data foundation for decision-making. By processing these data through scientific models and algorithms, the selected substation sites are more scientific and reasonable. A reasonable substation site selection helps to optimize the power grid layout, make the power supply more balanced and efficient, reduce the operation risks of the power grid, ensure the long-term stable operation of the power grid, and provide reliable power guarantee for the regional economic development and residents' lives. Brief Description of the Drawings

[0059] Figure 1 is a schematic flowchart of the substation site selection method in one embodiment of this application;

[0060] Figure 2 is a schematic diagram of the substation site selection system based on artificial intelligence in one embodiment of this application. Detailed Embodiments

[0061] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of this application more thorough and comprehensive. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the scope of protection of this application.

[0062] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0063] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0064] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as those commonly understood by those skilled in the technical field to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0065] An embodiment of the present application provides a substation site selection method based on artificial intelligence. Specifically, please refer to Figure 1 , Figure 1 which shows a schematic flow chart of the substation site selection method in one of the embodiments of the present application, including S1 - S5:

[0066] S1: Perform grid processing on the target area to obtain a certain number of grid units, and calculate the load density characteristic value of each grid unit based on the obtained expansion planning data of the target area;

[0067] Preferably, in an embodiment of the present application, the expansion planning data includes industrial layout data, population density data, and economic index data;

[0068] Calculating the load density characteristic value of each grid unit based on the obtained expansion planning data of the target area includes:

[0069] According to the obtained industrial layout data, population density data, and economic index data of each grid unit, calculate the load density characteristic value corresponding to each grid unit, expressed as:

[0070]

[0071] where No. The load density eigenvalue of a grid cell is the quantified value of the industrial layout data is the weight of the industrial layout data is the quantified value of the population density data is the weight of the population density data is the quantified value of the economic indicator data is the weight of the economic indicator data

[0072] Among them, the industrial layout data is obtained through government-related planning documents, industrial park statistical materials, etc. These data detail the distribution of each industry in the target area. For example, a grid cell is an industrial park, containing different industry types such as electronics manufacturing and machining; or it is a commercial area, concentrated with various commercial stores, office buildings, etc.

[0073] The population density data can be obtained from census materials and population distribution statistical data of the urban planning department. For each grid cell, the number of resident population is determined, and the population density is calculated according to the area of the grid cell

[0074] The economic indicator data is sourced from local economic statistical yearbooks, reports of economic development departments, etc. These indicators may include gross regional product, total industrial output value, commercial sales volume, etc. For each grid cell, the corresponding economic indicator value is obtained

[0075] In order to be able to uniformly incorporate different types of data into the model for calculating the load density eigenvalue, it is necessary to perform quantification processing on the industrial layout data, population density data, and economic indicator data. The setting of the weight determines the relative importance of the industrial layout data, population density data, and economic indicator data in calculating the load density eigenvalue. The determination of the weight usually requires comprehensive consideration of the actual situation of the target area and expert experience

[0076] After completing data acquisition, quantification, and weight determination, calculate the load density eigenvalue of each grid cell according to the formula

[0077] S2: Input the weather forecast data and load density eigenvalues of each obtained grid cell into a pre-constructed load prediction model for prediction to obtain the load prediction data set of the target area

[0078] Preferably, in an embodiment of the present application, before inputting the weather forecast data and load density eigenvalues of each obtained grid cell into a pre-constructed load prediction model for prediction, it further includes

[0079] Construct an initial load prediction model based on LSTM

[0080] The initial load prediction model is trained based on the obtained historical meteorological data, historical load data, and historical load density eigenvalue data. During the training process, the loss function is calculated according to the load prediction value obtained through forward propagation and the historical load data, and the parameters of the initial load prediction model are updated according to the value of the loss function to obtain a trained load prediction model.

[0081] Among them, the long short-term memory network (LSTM) is a special recurrent neural network (RNN) that can effectively handle the long-term dependence problem in time series data and is very suitable for the load prediction task.

[0082] After constructing the initial load prediction model, meteorological data for a past period in the target area is obtained from channels such as the meteorological department's database and professional meteorological data platforms, including information such as daily temperature, humidity, rainfall, and sunshine duration. Historical power load data corresponding to the target area is obtained from the power company's power consumption information collection system, and these data record the actual power consumption load values at different time points (such as every hour, every day). Based on data such as the industrial layout, population density, and economic indicators in the target area during historical periods, the load density eigenvalue data of each grid cell at historical time points is calculated according to the calculation method introduced previously.

[0083] Preprocess the historical data, and the preprocessing specifically includes data cleaning, data normalization, and data partitioning. Before inputting the training data into the initial load prediction model, the parameters in the model (such as the weight matrix and bias vector in the LSTM unit) are initialized.

[0084] The historical meteorological data and historical load density eigenvalue data in the training set are sequentially input into the initialized LSTM initial load prediction model. The data enters from the input layer in chronological order and is processed by the LSTM units in the hidden layer. Each LSTM unit updates the unit state and outputs the processed feature information through the control of the forget gate, input gate, and output gate according to the input data and the state of the previous moment, and finally obtains the load prediction value at the output layer.

[0085] Compare the load prediction value obtained through forward propagation with the corresponding historical load data in the training set, and calculate the loss function. The commonly used loss function is the mean square error (MSE). According to the value of the loss function, calculate the gradient of the loss function with respect to the model parameters through the backpropagation algorithm. The gradient represents the degree of influence of parameter changes on the value of the loss function. By continuously repeating the processes of forward propagation, calculating the loss function, backpropagation, and parameter update, the model parameters are gradually adjusted to continuously reduce the value of the loss function and improve the prediction ability of the model.

[0086] S3: Cluster the load prediction dataset to obtain a certain number of clustering regions, and calculate the load density of each clustering region;

[0087] Preferably, in an embodiment of the present application, clustering the load prediction dataset to obtain a certain number of clustering regions includes:

[0088] Cluster the load prediction dataset based on an improved K-means algorithm, which includes randomly selecting several sample data in the load prediction dataset as clustering centers;

[0089] During the calculation process, calculate the distance from each sample data in the load prediction dataset to the clustering center, and divide the sample data into the clustering cluster where the nearest clustering center is located according to the calculation result. Recalculate the mean value of the sample data in each clustering cluster as the new clustering center, where the distance includes spatial distance and feature distance;

[0090] Repeat the calculation process until the clustering center no longer changes, and obtain a certain number of initial clustering regions;

[0091] Calculate the silhouette coefficient of each sample data, evaluate the clustering quality of the initial clustering regions according to the silhouette coefficient, perform a secondary division on the initial clustering regions according to the result of the clustering quality evaluation, and obtain the clustering regions according to the result of the secondary division.

[0092] Randomly select several sample data from the load prediction dataset as the initial clustering centers. The determination of the number of clustering centers (K value) needs to comprehensively consider the data distribution characteristics and actual application requirements. For example, if there are several regions with significantly different electricity consumption characteristics in the target area (such as industrial areas, commercial areas, residential areas). Assume that the dataset contains the load prediction values of each grid cell at different time points and related features (such as a multi-dimensional data formed by a combination of meteorological data, load density characteristic values, etc.). The randomly selected sample data as clustering centers represent different types of initial cores in the data space.

[0093] For each sample data in the load prediction dataset, calculate its distance to each clustering center. Here, the distance includes spatial distance and feature distance, and is measured by comprehensively considering the position relationship and feature similarity in the multi-dimensional data space. For example, the Euclidean distance formula is used to calculate the spatial distance; for the feature distance, weights can be set according to the importance of the data features, and the differences in different feature dimensions are weighted and summed to obtain the comprehensive feature distance. According to the calculated distance, each sample data is divided into the clustering cluster where the nearest clustering center is located.

[0094] After dividing all the sample data, recalculate the mean of the sample data within each cluster and use it as the new cluster center. By continuously repeating the process of calculating distances, dividing clusters, and updating the cluster centers, the cluster centers will gradually move to locations that better represent the core positions of the clusters. Continue the above calculation process until the cluster centers no longer change. At this time, the obtained clusters form the initial clustering regions.

[0095] For each sample data, calculate its silhouette coefficient. The silhouette coefficient is used to measure the tightness of the sample data within its belonging cluster and the degree of separation from other clusters. Calculate the average silhouette coefficient of all sample data within each initial clustering region to evaluate the clustering quality of the entire clustering region. The higher the average silhouette coefficient, the better the consistency of the sample data within the clustering region and the better the clustering effect. According to the results of the clustering quality evaluation, perform a secondary division on the initial clustering regions. For clustering regions with a relatively low average silhouette coefficient, there may be a situation of mixed sample data and further subdivision is required. For example, if a clustering region contains sample data that should originally belong to different electricity consumption patterns (such as a mixture of industrial electricity and residential electricity), resulting in a low average silhouette coefficient, the clustering parameters can be readjusted (such as increasing the number of cluster centers), and the improved K-means algorithm can be used again to cluster the data within this region to divide it into more reasonable clustering regions. Through the secondary division, the final clustering regions are obtained, and these clustering regions can more accurately reflect the regional division of different electricity consumption characteristics within the target region.

[0096] Preferably, in an embodiment of the present application, calculating the load density of each clustering region includes:

[0097] Obtain the total load and the area of the region for all grid cells within the clustering region;

[0098] Calculate the initial load density of the clustering region based on the total load and the area;

[0099] Introduce a load distribution non-uniformity index to correct the initial load density to obtain the load density of the clustering region, where the non-uniformity index is the standard deviation or coefficient of variation of the load within the region.

[0100] For each clustering region, determine all the grid cells it contains. Through the previous load prediction data set and the rasterization processing information, summarize the load prediction values of all grid cells within the clustering region to obtain the total load. At the same time, calculate the area of the clustering region based on the area information of the grid cells and the number of grid cells covered by the clustering region. Calculate the initial load density of the clustering region based on the total load and the area.

[0101] In one embodiment of the present application, in order to more accurately reflect the actual situation of the load distribution within the clustering region, a load distribution non-uniformity index is introduced to correct the initial load density. Among them, the standard deviation reflects the degree of dispersion of the data. The larger the standard deviation, the more uneven the load distribution.

[0102] S4: Construct a Voronoi diagram of the target region based on the clustering region and the load density, and obtain the initial candidate points for the substation location in the target region based on the Voronoi diagram;

[0103] Preferably, in one embodiment of the present application, constructing a Voronoi diagram of the target region based on the clustering region and the load density includes:

[0104] Use the centroid of the clustering region as the initial seed point to construct an initial Voronoi diagram;

[0105] Adjust the boundary surface of the initial Voronoi diagram according to the load density. The adjustment is designed to make the boundary surface shift towards the clustering region with a lower load density;

[0106] Optimize the positions of the initial seed points in the adjusted initial Voronoi diagram based on the genetic algorithm to obtain the Voronoi diagram of the target region. The optimization is designed to make the load intensity of each Voronoi polygon balanced.

[0107] After completing the clustering region division and load density calculation, for each clustering region, it is necessary to calculate its centroid. The calculation method of the centroid will vary according to the geometric shape of the clustering region and the characteristics of the grid cells it contains. If the clustering region consists of multiple regular grid cells, the geometric centers of all grid cells can be used as the centroid of the clustering region. For example, for a clustering region composed of square grid cells, the horizontal and vertical coordinates of these grid cells in the plane rectangular coordinate system are averaged respectively, and the obtained coordinate point is the centroid of the clustering region.

[0108] Use the centroids of these clustering regions as the initial seed points and construct an initial Voronoi diagram using the Voronoi diagram generation algorithm. The Voronoi diagram generation algorithm will divide the plane space into multiple polygon regions according to the given seed points. The distance from any point within each polygon region to the corresponding seed point of that region is less than the distance to other seed points. In this process, the centroid of each clustering region becomes the core point of the corresponding Voronoi polygon, initially determining the spatial division range of each clustering region.

[0109] Considering the importance of load density in substation location, it is necessary to adjust the boundary surface of the initial Voronoi diagram to make it more conform to the load distribution situation. The specific method is to make the boundary surface shift towards the clustering region with a lower load density.

[0110] For two adjacent clustering regions A and B, there is an interface between their corresponding Voronoi polygons. Let the load density of region A be DA and the load density of region B be DB. If DA > DB, then move this interface a certain distance in the direction of region B. The determination of the moving distance can be calculated according to the degree of difference in load density.

[0111] The genetic algorithm is an optimization algorithm that simulates the biological evolution process and is used to further optimize the positions of the initial seed points to balance the load intensity of each Voronoi polygon.

[0112] Preferably, in an embodiment of the present application, obtaining the initial candidate points for the substation site selection in the target area based on the Voronoi diagram includes:

[0113] Calculating the deviation rate of the area of each Voronoi polygon in the Voronoi diagram from the average area;

[0114] If the deviation rate exceeds the preset deviation rate range, then re-optimize the position of the initial seed points based on the genetic algorithm; if the deviation rate is within the deviation rate range, then use the optimized position of the seed points as the initial candidate points.

[0115] For the constructed Voronoi diagram, calculate the area of each Voronoi polygon. The deviation rate reflects the degree of deviation of the area of each Voronoi polygon from the average area. Preset a deviation rate range, for example, [0, 0.1]. If the deviation rate of a certain Voronoi polygon exceeds the preset range, it indicates that the area of this polygon is quite different from the average area, which may lead to uneven distribution of substation site selections. At this time, re-optimize the position of the initial seed points based on the genetic algorithm. The optimization process is similar to the previous one. Through operations such as coding, fitness function design, selection, crossover, and mutation, adjust the position of the seed points, reconstruct the Voronoi diagram, and calculate the area and deviation rate of each Voronoi polygon again until the deviation rates of all Voronoi polygons are within the preset range.

[0116] When the deviation rates of all Voronoi polygons are within the deviation rate range, it indicates that the area distribution of the polygons in the Voronoi diagram is relatively uniform. At this time, use the optimized position of the seed points as the initial candidate points. These initial candidate points are relatively evenly distributed in space and consider the load density factor, providing a reasonable starting point for the multi-objective optimization of subsequent substation site selection.

[0117] S5: Construct a multi-objective optimization model for the substation site selection in the target area, and at least input the initial candidate points into the multi-objective optimization model for solution to obtain the substation site selection in the target area.

[0118] Preferably, in an embodiment of the present application, a multi-objective optimization model for the location selection of a target area substation is constructed, expressed as:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Among them, are the respective weight coefficients, is the maximum allowable value of electromagnetic radiation, is the maximum allowable value of noise, is the sum of the absolute differences between the actual power flow and the ideal power flow, is the total construction cost of the substation, is the total operating cost of the substation, is the average power outage time, is the average power outage frequency, is the total length of all transmission lines between substations, is the comprehensive optimization objective, is the economic objective, is the reliability objective, is the environmental impact objective, is the power grid structure objective.

[0129] Specifically, the multi-objective optimization model comprehensively considers multiple key factors to determine the optimal substation location selection plan. Economic objective (C): It covers the total construction cost of the substation (including equipment procurement, land acquisition, construction costs, etc.) and the total operating cost (such as energy loss, equipment maintenance costs, etc.). The construction cost can be obtained by calculating different equipment prices, land market prices, and construction cost standards; the operating cost needs to consider factors such as the service life of the equipment, energy price fluctuations, and maintenance cycles and costs.

[0130] Reliability objective (R): Measured by System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI). SAIDI reflects the average duration of power outages for users during the statistical period, while SAIFI reflects the number of power outages that occur during the statistical period. Reducing these two indicators can improve the reliability of power supply and ensure normal power consumption for users.

[0131] Environmental impact objective (E): Reflected by limiting the electromagnetic radiation intensity not exceeding the allowable maximum value and the noise not exceeding the allowable maximum value. When selecting the location of a substation, the sensitivity of the surrounding environment needs to be considered, such as areas sensitive to electromagnetic radiation and noise like residential areas, schools, hospitals, etc., to ensure that the impact of substation operation on the environment and residents' lives is within an acceptable range.

[0132] Power grid structure objective (S): Measured by the sum of the absolute differences between the actual power flow and the ideal power flow and the total length of all transmission lines between substations. The difference between the actual power flow and the ideal power flow reflects the stability of power grid operation and the rationality of load distribution; the total length of transmission lines is closely related to construction costs, transmission losses, etc. A shorter total length of transmission lines helps reduce costs and losses.

[0133] Preferably, in an embodiment of the present application, at least the initial candidate points are input into the multi-objective optimization model for solution to obtain the substation location in the target area, including:

[0134] Taking the initial candidate points as the initial population individuals and solving them based on the improved genetic algorithm in the multi-objective optimization model to obtain a candidate solution set; where the candidate solution set corresponds to the set of position coordinates of the candidate substations;

[0135] Sorting the candidate solution set based on the TOPSIS method and screening according to the sorting result to obtain the substation location in the target area.

[0136] Taking the initial candidate points obtained based on the Voronoi diagram as the initial population individuals and inputting them into the multi-objective optimization model. Each individual is composed of a set of position coordinates of the candidate substation, and these coordinates represent potential substation location positions.

[0137] The improved genetic algorithm operations include selection operation, crossover operation, and mutation operation.

[0138] Selection operation: Select individuals with higher fitness from the current population as parents for breeding the next generation. In multi-objective optimization, the fitness evaluation cannot be based solely on a single objective function, but multiple objectives need to be considered comprehensively. Here, a selection strategy based on Pareto dominance relationship and crowding distance is adopted. The Pareto dominance relationship is used to determine the superiority and inferiority of individuals in multiple objectives. If individual A is not worse than individual B in all objectives and is superior to individual B in at least one objective, then individual A is said to dominate individual B. The crowding distance measures the degree of crowding of an individual on its Pareto front. The larger the distance, the sparser the distribution of solutions around the individual, indicating better diversity. Through this selection strategy, individuals that perform well in multiple objectives and are evenly distributed are preferentially selected as parents.

[0139] Crossover operation: Perform crossover operations on the selected parent individuals to generate new offspring individuals. For individuals representing the substation location coordinates, arithmetic crossover and other methods can be used.

[0140] Mutation operation: Mutate the genes of individuals (i.e., substation location coordinates) with a certain probability. Gaussian mutation and other methods can be used for the mutation operation to introduce new genes into the population, increase the diversity of the population, and prevent the algorithm from falling into local optimal solutions.

[0141] Iteration termination condition: Set iteration termination conditions, such as reaching the maximum number of iterations (e.g., 1000 times) or the optimal solution of the population has not improved significantly for several consecutive generations (e.g., 50 generations). When the termination condition is met, the algorithm stops iterating and obtains a candidate solution set, which contains a series of substation site selection schemes that achieve a better balance in multiple objectives.

[0142] TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution), that is, the method for ranking by approaching the ideal solution, ranks the candidate solutions by calculating the distances between each solution and the ideal solution and the negative ideal solution. The ideal solution is the solution that achieves the best in all objectives, and the negative ideal solution is the solution that achieves the worst in all objectives.

[0143] The calculation steps of the TOPSIS method include standardizing the objective values of each solution in the candidate solution set to eliminate the influence of different objective dimensions; for each objective, finding the optimal value and the worst value in the candidate solution set; calculating the Euclidean distances between each solution and the ideal solution and the negative ideal solution; and calculating the relative closeness degree of each solution according to the Euclidean distance.

[0144] The candidate solution set is sorted according to the relative closeness calculated by the TOPSIS method, and the solution with a higher relative closeness is selected as the preferred location of the substation. For example, the top 3 solutions with the highest relative closeness are selected. After further on-site inspections, feasibility analyses, etc., the substation site selection in the target area is finally determined. These locations achieve a better balance among multiple objectives such as economy, reliability, environmental impact, and power grid structure, providing a strong basis for the scientific site selection of the substation.

[0145] Another embodiment of the present application provides an artificial intelligence-based substation site selection system. Specifically, please refer to Figure 2 , Figure 2 which shows a schematic diagram of the artificial intelligence-based substation site selection system in one of the embodiments of the present application, and it includes:

[0146] A calculation module 11, configured to perform grid division on the target area to obtain a certain number of grid units, and calculate the load density characteristic value of each grid unit based on the obtained expansion planning data of the target area;

[0147] A prediction module 12, configured to input the obtained meteorological forecast data and load density characteristic values of each grid unit into a pre-constructed load prediction model for prediction to obtain a load prediction data set of the target area;

[0148] A clustering module 13, configured to cluster the load prediction data set to obtain a certain number of clustering regions, and calculate the load density of each clustering region;

[0149] A construction module 14, configured to construct a Voronoi diagram of the target area based on the clustering regions and load density, and obtain the initial candidate points for the substation site selection in the target area based on the Voronoi diagram;

[0150] A generation module 15, configured to construct a multi-objective optimization model for the substation site selection in the target area, and input at least the initial candidate points into the multi-objective optimization model for solution to obtain the substation site selection in the target area.

[0151] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0152] (1) By clustering the load forecasting data set, this application divides different clustering regions and calculates the load density of each region. On this basis, a Voronoi diagram is constructed. This diagram can naturally divide the target area into multiple sub-regions according to the load distribution characteristics. The distance from each point in each sub-region to the nearest clustering center (i.e., the potential load concentration point) is the shortest. The initial candidate points obtained based on the Voronoi diagram are naturally distributed in and around the load concentration area. Compared with randomly selecting candidate points, it greatly reduces the search space in the subsequent optimization process, improves the site selection efficiency, and can find the substation location that meets the requirements faster.

[0153] (2) The entire site selection process of this application is based on a large amount of data processing and analysis, from the expansion planning data, meteorological data to the load forecasting data of the target area, etc., providing a solid data basis for decision-making. By processing these data through scientific models and algorithms, the substation site selection obtained is more scientific and reasonable. A reasonable substation site selection helps to optimize the power grid layout, make the power supply more balanced and efficient, reduce the operation risk of the power grid, ensure the long-term stable operation of the power grid, and provide reliable power guarantee for regional economic development and residents' lives.

[0154] The above embodiments only represent several implementation manners of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several deformations and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application should be subject to the appended claims.

Claims

1. A substation site selection method based on artificial intelligence, characterized in that Including: Rasterize the target area to obtain a number of grid cells, and calculate the load density eigenvalue of each grid cell based on the obtained expansion planning data of the target area; Input the obtained meteorological forecast data and load density eigenvalues of each grid cell into a pre-constructed load prediction model for prediction to obtain the load prediction data set of the target area; Cluster the load prediction data set to obtain a number of cluster areas, and calculate the load density of each cluster area; Construct a Voronoi diagram of the target area based on the cluster area and the load density, and obtain the initial candidate points for substation site selection in the target area based on the Voronoi diagram; Construct a multi-objective optimization model for substation site selection in the target area, and input at least the initial candidate points into the multi-objective optimization model for solution to obtain the substation site selection in the target area.

2. The substation site selection method based on artificial intelligence according to claim 1, characterized in that The expansion planning data includes industrial layout data, population density data, and economic indicator data; The calculating the load density eigenvalue of each grid cell based on the obtained expansion planning data of the target area includes: According to the obtained industrial layout data, population density data, and economic indicator data of each grid cell, calculate the load density eigenvalue corresponding to each grid cell, expressed as: Among them, The load density eigenvalue of the quantization value of the industrial layout data, is the weight of the industrial layout data, is the quantization value of the population density data, is the weight of the population density data, is the quantization value of the economic indicator data, is the weight of the economic indicator data.

3. The substation site selection method based on artificial intelligence according to claim 1, wherein, Before inputting the obtained meteorological forecast data and load density eigenvalues of each grid cell into a pre-constructed load prediction model for prediction, it further includes: Construct an initial load prediction model based on LSTM; Train the initial load prediction model based on the obtained historical meteorological data, historical load data, and historical load density eigenvalue data. During the training process, calculate the loss function according to the load prediction value obtained by forward propagation and the historical load data, and update the parameters of the initial load prediction model according to the value of the loss function to obtain a trained load prediction model.

4. The method for substation site selection based on artificial intelligence according to claim 1, wherein The clustering the load prediction data set to obtain a number of cluster areas includes: Cluster the load prediction data set based on an improved K-means algorithm, which includes randomly selecting several sample data in the load prediction data set as cluster centers; During the calculation, calculate the distance from each sample data in the load prediction data set to the cluster center, and divide the sample data into the cluster where the nearest cluster center is located according to the calculation result, and recalculate the sample data mean of each cluster as the new cluster center, where the distance includes spatial distance and feature distance; Repeat the calculation process until the cluster centers no longer change to obtain a number of initial cluster areas; Calculate the silhouette coefficient of each sample data, evaluate the clustering quality of the initial cluster areas according to the silhouette coefficient, perform secondary division on the initial cluster areas according to the result of the clustering quality evaluation, and obtain the cluster areas according to the result of the secondary division.

5. The method for substation site selection based on artificial intelligence according to claim 1, wherein The calculating the load density of each cluster area includes: Obtain the total load and the area of all grid cells within the clustering region; Calculate the initial load density of the clustering region based on the total load and the area of the region; Introduce a load distribution non-uniformity index to correct the initial load density to obtain the load density of the clustering region, where the non-uniformity index is the standard deviation or coefficient of variation of the load within the region.

6. The substation site selection method based on artificial intelligence according to claim 1, characterized in that Constructing the Voronoi diagram of the target region based on the clustering region and the load density includes: Use the centroid of the clustering region as the initial seed point to construct an initial Voronoi diagram; Adjust the boundary surface of the initial Voronoi diagram according to the load density, and the adjustment is designed to make the boundary surface shift towards the clustering region with a lower load density; Optimize the position of the initial seed points in the adjusted initial Voronoi diagram based on a genetic algorithm to obtain the Voronoi diagram of the target region, and the optimization is designed to balance the load intensity of each Voronoi polygon.

7. The substation site selection method based on artificial intelligence according to claim 1, characterized in that, Obtaining the initial candidate points for the substation location in the target region based on the Voronoi diagram includes: Calculate the deviation rate of the area of each Voronoi polygon in the Voronoi diagram from the average area; If the deviation rate exceeds the preset deviation rate range, re-optimize the position of the initial seed points based on a genetic algorithm; if the deviation rate is within the deviation rate range, use the optimized seed point position as the initial candidate point.

8. The substation site selection method based on artificial intelligence according to claim 1, characterized in that Constructing the multi-objective optimization model for the substation location in the target region is expressed as: Among them, are the respective weight coefficients, is the maximum allowable value of electromagnetic radiation, is the maximum allowable value of noise, is the sum of the absolute differences between the actual power flow and the ideal power flow, is the total construction cost of the substation, is the total operating cost of the substation, is the average power outage time, is the average power outage frequency, is the total length of all transmission lines between substations, is the comprehensive optimization goal, is the economic goal, is the reliability goal, is the environmental impact goal, is the power grid structure goal.

9. The substation site selection method based on artificial intelligence according to claim 1, characterized in that, At least input the initial candidate points into the multi-objective optimization model for solution to obtain the substation location in the target region, including: Input the initial candidate points as initial population individuals into the multi-objective optimization model for solution based on an improved genetic algorithm to obtain a candidate solution set; where the candidate solution set corresponds to the set of position coordinates of the candidate substations; Rank the candidate solution set based on the TOPSIS method and perform screening according to the ranking result to obtain the substation location in the target region.

10. An artificial intelligence-based substation site selection system, characterized in that, Including: A calculation module for rasterizing the target region to obtain a certain number of grid cells, and calculating the load density characteristic value of each grid cell based on the obtained expansion planning data of the target region; A prediction module for inputting the obtained meteorological forecast data and load density characteristic values of each grid cell into a pre-constructed load prediction model for prediction to obtain the load prediction data set of the target region; A clustering module for clustering the load prediction data set to obtain a certain number of clustering regions, and calculating the load density of each clustering region; A construction module for constructing the Voronoi diagram of the target region based on the clustering region and the load density, and obtaining the initial candidate points for the substation location in the target region based on the Voronoi diagram; A generation module, configured to construct a multi-objective optimization model for the substation site selection in the target area, and at least input the initial candidate points into the multi-objective optimization model for solution to obtain the substation site selection in the target area.

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