Energy storage node configuration method and system based on space-time analysis

Abstract

The application relates to the technical field of power distribution network management, and particularly discloses a method and system for configuring energy storage nodes based on space-time analysis, which comprises the following steps: obtaining power distribution transformers of a target area and power lines based on the power distribution transformers, obtaining output points of the power lines, and determining monitoring points in the power lines according to the output points; obtaining line data and performing two-dimensional processing on the line data; determining spatial abnormality degrees of the output points based on the two-dimensionally processed line data; determining time abnormality degrees of the output points based on the line data of each output point; and determining energy storage nodes by counting the spatial abnormality degrees and the time abnormality degrees of all the output points. The application performs space-time analysis on the data of all the output points, selects some unstable output points, additionally arranges some energy storage nodes based on the output points, and supplies energy to the output points, so that the stability of the entire power supply network is improved.

Description

[0001] This application is a divisional application of the invention application filed on December 4, 2024, with Chinese application number 202411769130.1 and entitled "A microgrid configuration method and system for weak distribution areas in mountainous regions". Technical Field

[0002] This invention relates to the field of power distribution network management technology, specifically a method and system for configuring energy storage nodes based on spatiotemporal analysis. Background Technology

[0003] "Distribution area" is a term in the power system that refers to an area supplied by a power transformer. Specifically, a distribution area is a defined power supply range within a power distribution network, supplied by one or more distribution transformers. Each distribution area typically serves a certain number of users, who share the power supply from one or more transformers.

[0004] In mountainous areas, power transmission is affected by the environment, and some power supply processes are very unstable, with weak distribution transformers affecting users' lives. How to improve the power supply stability of weak distribution transformers in mountainous areas is the technical problem that this invention aims to solve. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for configuring energy storage nodes based on spatiotemporal analysis, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The system acquires the distribution transformers and power lines based on the distribution transformers in the target area, obtains the output points of the power lines, and determines the monitoring points in the power lines based on the output points.

[0008] The line data is acquired and processed into two dimensions; wherein the line data is acquired by instruments installed at the output point and the detection point.

[0009] Based on the two-dimensional processed line data, the spatial anomaly of each output point is determined; whereby the spatial anomaly represents the difference between the data of any location at the current time and the data of its adjacent locations at the current time.

[0010] Based on the line data at each output point, the time anomaly degree of each output point is determined; where the time anomaly degree represents the fluctuation of line data over a period of time.

[0011] Statistically analyze the spatial and temporal anomalies of all output points to determine the energy storage nodes.

[0012] As a further aspect of the present invention: the steps of acquiring the distribution transformer of the target area and the power lines based on the distribution transformer, acquiring the output points of the power lines, and determining the monitoring points in the power lines based on the output points include:

[0013] Obtain the distribution transformers installed in the target area, and use the distribution transformers as line nodes to query the power lines within the transformer area;

[0014] Obtain the output points on the power line and calculate the detection density at each location on the power line based on the output points;

[0015] Monitoring points are selected based on detection density, and the detection density is updated synchronously.

[0016] The process is repeated until a preset loop exit condition is met; the loop exit condition includes the detection density at all locations reaching a preset density value and the number of monitoring points reaching a preset number threshold.

[0017] As a further aspect of the present invention: the calculation process of the detection density includes:

[0018] In the formula, ρ(x) is the detection density at position x on the power line, α is the preset correction coefficient, and d i (x) is the distance between the i-th point and position x, and N is the total number of points; the points include output points and monitoring points.

[0019] As a further aspect of the present invention: the step of acquiring line data and performing two-dimensional processing on the line data includes:

[0020] Establish connection channels with instruments installed at output points and with instruments installed at monitoring points;

[0021] Obtain line data containing location tags based on the established connection channels;

[0022] Perform time-domain registration on the line data and standardize the line data containing location labels at the same time.

[0023] Data is arranged and normalized based on location labels.

[0024] As a further aspect of the present invention: the standardization process includes:

[0025] In the formula, y ′ Here, y represents the data after normalization, and y represents the data before normalization. max y represents the maximum value of the line data. min This represents the minimum value of the line data.

[0026] As a further aspect of the present invention: the step of determining the spatial anomaly of each output point based on the two-dimensional processed line data includes:

[0027] Perform a two-dimensional Fourier transform on the line data after two-dimensional processing to extract the spectrum and phase diagram;

[0028] High-pass filtering of different sizes is performed on the spectrum graph, and inverse transformation of the high-pass filtered spectrum graph is performed based on the phase graph.

[0029] In the result obtained from the inverse transformation, find the location of the retained data;

[0030] The spatial anomaly of the location of the retained data is determined based on the size of the high-pass filter.

[0031] Among them, the high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size is the radius of the circle; the spatial anomaly is proportional to the maximum radius corresponding to each location.

[0032] As a further aspect of the present invention: the step of determining the time anomaly degree of each output point based on the line data of each output point includes:

[0033] Statistically analyze the line data for each output point and calculate the data difference of the line data;

[0034] Starting from the current time, query the data difference within the preset backtracking time.

[0035] Perform extreme value removal processing on the data differences within the backtracking time according to a preset ratio, and calculate the standard deviation of the data differences after extreme value removal processing;

[0036] The time anomaly of the output point is determined based on the direct proportion to the standard deviation.

[0037] As a further aspect of the present invention: the step of determining the energy storage node by statistically analyzing the spatial and temporal anomalies of all output points includes:

[0038] Statistically calculate the spatial and temporal anomalies of all output points, and then calculate the overall anomaly.

[0039] The predicted energy storage area is determined based on the comprehensive anomaly degree; the predicted energy storage area is a circular region, and the radius of the circular region is inversely proportional to the comprehensive anomaly degree. The predicted energy storage area is used to characterize the installation range of the energy storage node that supplies power to the output point.

[0040] Calculate the intersection of all predicted energy storage areas, and sort the intersections in descending order based on the number of predicted energy storage areas corresponding to each intersection.

[0041] Install energy storage nodes sequentially within the intersection until the number of energy storage nodes reaches a preset threshold.

[0042] As a further aspect of the present invention: during the installation of energy storage nodes, each time an energy storage node is installed, the predicted energy storage area corresponding to that energy storage node is removed once during the calculation of the intersection of all predicted energy storage areas.

[0043] The present invention also provides an energy storage node configuration system based on spatiotemporal analysis, the system comprising:

[0044] The monitoring point determination module acquires the distribution transformers and power lines based on the distribution transformers in the target area, acquires the output points of the power lines, and determines the monitoring points in the power lines based on the output points.

[0045] A two-dimensional processing module acquires line data and performs two-dimensional processing on the line data; wherein, the line data is acquired by instruments installed at the output point and the detection point;

[0046] The spatial analysis module, based on the two-dimensional processed line data, determines the spatial anomaly of each output point; where spatial anomaly represents the difference between the data of any location at the current time and the data of its adjacent locations at the current time.

[0047] The time analysis module determines the time anomaly degree of each output point based on the line data of each output point; where time anomaly degree represents the fluctuation of line data over a period of time.

[0048] As a further aspect of the present invention: the monitoring point determination module includes:

[0049] The data query unit is used to obtain the distribution transformers installed in the target area, and to query the power lines in the target area using the distribution transformers as line nodes;

[0050] The detection density calculation unit is used to obtain the output points on the power line and calculate the detection density at each location on the power line based on the output points.

[0051] The detection density update unit is used to select monitoring points based on the detection density and update the detection density synchronously.

[0052] The loop execution unit is used to execute repeatedly until a preset loop exit condition is met; the loop exit condition includes the detection density of all locations reaching a preset density value and the number of monitoring points reaching a preset number threshold.

[0053] As a further aspect of the present invention: the two-dimensional processing module includes:

[0054] The channel establishment unit is used to establish connection channels with instruments installed at output points and with instruments installed at monitoring points.

[0055] The channel application unit is used to obtain line data containing location tags based on the established connection channel;

[0056] The normalization processing unit is used to perform time-domain registration on line data, and to normalize line data containing location labels at the same time.

[0057] Data sorting unit, used to sort normalized data based on position labels.

[0058] As a further aspect of the present invention: the spatial analysis module includes:

[0059] The two-dimensional data processing unit is used to perform two-dimensional Fourier transform on the two-dimensional processed line data to extract the spectrum and phase diagram;

[0060] The inverse transform unit is used to perform high-pass filtering of the spectrum graph at different sizes, and to perform inverse transform of the high-pass filtered spectrum graph based on the phase graph.

[0061] The location query unit is used to query the location of the retained data in the result obtained from the inverse transformation;

[0062] Anomaly calculation unit, used to determine the spatial anomaly of the location of the retained data based on the size of the high-pass filter;

[0063] Among them, the high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size is the radius of the circle; the spatial anomaly is proportional to the maximum radius corresponding to each location.

[0064] As a further aspect of the present invention: the time analysis module includes:

[0065] The data difference calculation unit is used to statistically analyze the line data at each output point and calculate the data difference of the line data.

[0066] The data difference query unit is used to query the data difference within a preset backtracking time, starting from the current time.

[0067] The mean calculation unit is used to perform a preset ratio of extremum removal on the data differences within the backtracking time and calculate the standard deviation of the data differences after extremum removal.

[0068] An execution unit is used to determine the time anomaly of the output point based on a direct proportion to the standard deviation.

[0069] As a further aspect of the present invention: the energy storage node setting module includes:

[0070] The data statistics unit is used to count the spatial and temporal anomalies of all output points and calculate the overall anomaly.

[0071] A prediction unit is used to determine a predicted energy storage area based on the comprehensive anomaly degree; the predicted energy storage area is a circular region, the radius of which is inversely proportional to the comprehensive anomaly degree, and the predicted energy storage area is used to characterize the installation range of the energy storage node that supplies power to the output point.

[0072] The descending sorting unit is used to calculate the intersection of all predicted energy storage areas and sorts the intersection in descending order according to the number of predicted energy storage areas corresponding to each intersection.

[0073] The installation application unit is used to install energy storage nodes in the intersection in sequence until the number of energy storage nodes reaches a preset threshold.

[0074] Compared with the prior art, the beneficial effects of the present invention are:

[0075] This invention performs spatiotemporal analysis on the data from all output points, selects some unstable output points, and deploys additional energy storage nodes based on these output points to supply energy to them, thereby improving the stability of the entire power supply network. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0077] Figure 1 A general flowchart of the energy storage node configuration method based on spatiotemporal analysis is shown.

[0078] Figure 2 A structural diagram of an energy storage node configuration system based on spatiotemporal analysis is shown. Detailed Implementation

[0079] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0080] Example 1:

[0081] Figure 1 This invention provides a general flowchart of an energy storage node configuration method and system based on spatiotemporal analysis. In this embodiment, an energy storage node configuration method based on spatiotemporal analysis is provided, the method comprising:

[0082] Step S100: Obtain the distribution transformers and power lines based on the distribution transformers in the target area (in this embodiment, the transformer substation); obtain the output points of the power lines; and determine the monitoring points in the power lines based on the output points.

[0083] Step S200: Obtain line data based on the instruments installed at the output points and the instruments installed at the monitoring points, and perform two-dimensional processing on the line data;

[0084] Step S300: Identify the two-dimensional processed line data and determine the spatial anomaly of each output point;

[0085] Step S400: Identify the line data of each output point and determine the time anomaly degree of each output point;

[0086] Step S500: Calculate the spatial and temporal anomalies of all output points to determine the energy storage nodes.

[0087] "Distribution area" is a term in the power system that refers to an area supplied by a power transformer. Specifically, a distribution area is a defined power supply range within a power distribution network, supplied by one or more distribution transformers. Each distribution area typically serves a certain number of users, who share the power supply from one or more transformers.

[0088] The distribution transformers and power lines based on them in each distribution area already have data that can be read directly. Based on this, we can determine which power-consuming units the power lines are connected to, which are called output points. There will always be a meter at each output point. Based on the distribution of output points, we can add some monitoring points to the power lines to make the data acquisition process more comprehensive.

[0089] Line data is acquired using instruments installed at output and monitoring points. Each line data point represents either a monitoring or output point, and its location is recorded. The line data is then sorted based on its location and processed into two dimensions. After two-dimensional processing, the data is identified to pinpoint unusual locations, known as spatial anomalies. Spatial anomalies represent the difference between the data at any given location at the current moment and the data at adjacent locations at the same moment. Generally, if there are branching paths between adjacent locations, the differences between their line data will be significant.

[0090] For each installed instrument location, the acquired line data is arranged in chronological order, and the fluctuation of the line data is analyzed to obtain the time anomaly degree; the time anomaly degree represents the fluctuation of the line data over a period of time.

[0091] Finally, some output points are selected based on spatial and temporal anomalies, and some energy storage nodes are set up based on these output points to compensate for their electrical energy and improve their stability during use.

[0092] The intended function of this invention is to select some unstable output points from multiple output points, and to deploy additional energy storage nodes based on these output points to supply energy to them, thereby improving the stability of the entire power supply network.

[0093] Example 2:

[0094] Regarding step S100, the steps of obtaining the distribution transformers of the target area (in this embodiment, the transformer substation) and the power lines based on the distribution transformers, obtaining the output points of the power lines, and determining the monitoring points in the power lines based on the output points include:

[0095] Obtain the distribution transformers installed in the target area (in this embodiment, the transformer substation), and use the distribution transformers as line nodes to query the power lines within the target area (in this embodiment, the transformer substation);

[0096] Obtain the output points on the power line and calculate the detection density at each location on the power line based on the output points;

[0097] Monitoring points are selected based on detection density, and the detection density is updated synchronously.

[0098] The process is repeated until a preset loop exit condition is met; the loop exit condition includes the detection density at all locations reaching a preset density value and the number of monitoring points reaching a preset number threshold.

[0099] The above content specifies the selection process for monitoring points. The ultimate goal of this application is to analyze the output points. Since the power consumption data of the instruments installed at the output points is known, it is also feasible to analyze the output points directly without installing monitoring points, but the comprehensiveness of the data will be slightly reduced. Therefore, this application also provides a monitoring point selection scheme to improve the comprehensiveness of the data acquisition process.

[0100] The distribution transformers installed in the target area (in this embodiment, the transformer substation) are obtained. The power lines in the target area (in this embodiment, the transformer substation) are queried using the distribution transformers as line nodes. The distribution transformers are devices that output power. The power lines are connected to the power-consuming units. The output points on the power lines are obtained. The detection density at each location in the power lines is calculated based on the output points. The detection density indicates how many detection resources are available at a certain location.

[0101] Compare the detection density at each location, install a monitoring point at the location with the lowest detection density, and then update the detection density at all locations. Then install a monitoring point at the location with the lowest detection density again, and repeat the process until the detection density at all locations reaches the preset density value or the number of monitoring points reaches the preset number threshold.

[0102] Furthermore, the calculation process for the detection density includes:

[0103] In the formula, ρ(x) is the detection density at position x on the power line, α is the preset correction coefficient, and d i (x) is the distance between the i-th point and position x, and N is the total number of points; the points include output points and monitoring points.

[0104] The x-position in the density calculation process needs to be explained. This application considers the line to be one-dimensional. Therefore, the line needs to be simplified into a single line in a preset order. After selecting an origin on the line, all positions can be represented by the single parameter x. Each point with an instrument will affect the density at position x. The farther the distance, the smaller the influence. For any position, the influence of all points with instruments at that position is superimposed to obtain the final detection density.

[0105] Example 3:

[0106] Step S200, the step of acquiring line data based on the instruments installed at the output point and the instruments installed at the monitoring point, and performing two-dimensional processing on the line data, includes:

[0107] Establish connection channels with instruments installed at output points and with instruments installed at monitoring points;

[0108] Obtain line data containing location tags based on the established connection channels;

[0109] Perform time-domain registration on the line data and standardize the line data containing location labels at the same time.

[0110] Data is arranged and normalized based on location labels.

[0111] Existing instruments all have data transmission capabilities. A connection channel is established with the instrument, and line data containing location tags is acquired based on the established connection channel. In the technical solution of this invention, all instruments have the same data acquisition frequency. However, since the transmission process itself takes time, the line data acquired from different instruments will have a time offset. In this case, line data with a sufficiently small time difference needs to be regarded as data at the same moment, that is, time domain registration. For line data at the same moment, they are arranged according to the position of the instrument to obtain two-dimensional data. Generally, from north to south corresponds to from top to bottom, and from west to east corresponds to from left to right.

[0112] In addition, for ease of processing, this application will also perform normalization processing on the line data during the process of arranging the normalized data based on the location labels, that is, perform dimensionless processing on the line data.

[0113] Furthermore, the normalization process includes:

[0114] In the formula, y ′ Here, y represents the data after normalization, and y represents the data before normalization. max y represents the maximum value of the line data. min This represents the minimum value of the line data.

[0115] The normalization process itself is very simple. It is worth mentioning that the normalized data will fall into the range of 0 to 255. This value is actually the range of grayscale values, which makes the two-dimensional data compatible with most image processing algorithms for calculating spatial anomalies.

[0116] Example 4:

[0117] Regarding step S300, the step of identifying the spatial anomaly of each output point after the two-dimensional processing of the line data includes:

[0118] Perform a two-dimensional Fourier transform on the line data after two-dimensional processing to extract the spectrum and phase diagram;

[0119] High-pass filtering of different sizes is performed on the spectrum graph, and inverse transformation of the high-pass filtered spectrum graph is performed based on the phase graph.

[0120] In the result obtained from the inverse transformation, find the location of the retained data;

[0121] The spatial anomaly of the location of the retained data is determined based on the size of the high-pass filter.

[0122] Among them, the high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size is the radius of the circle; the spatial anomaly is proportional to the maximum radius corresponding to each location.

[0123] The above content provides specific limitations on the calculation process of spatial anomaly. The line data after two-dimensional processing can be compared to an image. Performing a two-dimensional Fourier transform on the line data after two-dimensional processing yields a spectrum and a phase diagram. The spectrum reflects the changes in each data point after two-dimensional processing. The high-frequency part corresponds to the part with drastic changes. By performing a high-pass filter on the spectrum, that is, removing the low-frequency part (around the origin in the frequency domain diagram), the positions with more drastic changes can be retained.

[0124] In the process of removing low-frequency components, the size of the area removed is determined by the dimensions of the aforementioned content. The larger the size, the more drastic the changes in the retained positions, and correspondingly, the higher the spatial anomaly. Since the same position may be retained in the removal process of multiple sizes, the largest size is selected, and the spatial anomaly is determined proportionally to the largest size. The function selected for proportionality can be a composite function based on exponential functions, a composite function based on logarithmic functions, or a composite function based on power functions. The specific parameters are determined by the staff as needed.

[0125] Example 5:

[0126] Regarding step S400, the step of identifying the line data of each output point and determining the time anomaly degree of each output point includes:

[0127] Statistically analyze the line data for each output point and calculate the data difference of the line data;

[0128] Starting from the current time, query the data difference within the preset backtracking time.

[0129] Perform extreme value removal processing on the data differences within the backtracking time according to a preset ratio, and calculate the standard deviation of the data differences after extreme value removal processing;

[0130] The time anomaly of the output point is determined based on the direct proportion to the standard deviation.

[0131] In one example of the technical solution of this invention, the calculation process of time anomaly is described. The calculation process of time anomaly is actually a fluctuation identification process. The line data of each output point is statistically analyzed, and the data difference of the line data is obtained. The data difference is the derivative of discrete data, which reflects the change. Taking the current time as the starting point, the data difference within a preset backtracking time is queried. The backtracking time is generally one day. The data difference within the backtracking time is subjected to a preset ratio of extreme value removal processing, and the standard deviation of the data difference after extreme value removal processing is calculated. The extreme value removal processing is simply to remove a certain number of maximum values ​​and a certain number of minimum values. The purpose of extreme value removal processing is to prevent misjudgment (due to violent fluctuations caused by noise). The data difference after extreme value removal processing is analyzed to calculate the standard deviation. The time anomaly of the output point can be determined according to the direct proportion of the standard deviation. That is, the larger the standard deviation, the greater the fluctuation and the greater the time anomaly.

[0132] Furthermore, the step of determining the energy storage nodes by statistically analyzing the spatial and temporal anomalies of all output points includes:

[0133] Statistically calculate the spatial and temporal anomalies of all output points, and then calculate the overall anomaly.

[0134] The predicted energy storage area is determined based on the comprehensive anomaly degree; the predicted energy storage area is a circular region, and the radius of the circular region is inversely proportional to the comprehensive anomaly degree. The predicted energy storage area is used to characterize the installation range of the energy storage node that supplies power to the output point.

[0135] Calculate the intersection of all predicted energy storage areas, and sort the intersections in descending order based on the number of predicted energy storage areas corresponding to each intersection.

[0136] Install energy storage nodes sequentially within the intersection until the number of energy storage nodes reaches a preset threshold.

[0137] In one example of the technical solution of this invention, the spatial anomaly and temporal anomaly of all output points are statistically analyzed, and the comprehensive anomaly is calculated (by summing them according to a preset weight). Based on the comprehensive anomaly, a radius is determined, and a circular area centered on the output point is created, called the predicted energy storage area. The larger the comprehensive anomaly, the smaller the radius, indicating the range within which energy supply points need to be set when providing auxiliary energy to the output point.

[0138] After determining the predicted energy storage areas for all output points, calculate the intersection of all predicted energy storage areas. The number of predicted energy storage areas corresponding to each intersection is different. The more predicted energy storage areas there are, the more significant it is to set up energy storage nodes at that intersection. Sort the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection, and then select the intersections in sequence and install energy storage nodes in the intersections.

[0139] It should be noted that the predicted energy storage area is itself a spatial range, and the intersection is also a range. Energy storage nodes can be set within the intersection by randomization, as long as the energy storage nodes are within the intersection.

[0140] Example 6:

[0141] During the installation of energy storage nodes, for each energy storage node installed, the predicted energy storage area corresponding to that energy storage node is removed once during the calculation of the intersection of all predicted energy storage areas.

[0142] The above content adds a simplified scheme: for each energy storage node set up, the corresponding predicted energy storage area is deleted, indicating that the corresponding output node already has auxiliary power supply equipment. This can prevent the energy storage nodes from being too concentrated, that is, an output node can be powered by multiple energy storage nodes.

[0143] In fact, both solutions provided in this application are feasible; the only difference lies in the number of energy storage nodes required and the associated costs.

[0144] Example 7:

[0145] Figure 2 A structural diagram of an energy storage node configuration system based on spatiotemporal analysis is shown. In a preferred embodiment of the technical solution of the present invention, an energy storage node configuration system based on spatiotemporal analysis is also provided, the system 10 comprising:

[0146] The monitoring point determination module 11 is used to acquire the distribution transformers and power lines based on the distribution transformers in the target area (in this embodiment, the transformer substation), acquire the output points of the power lines, and determine the monitoring points in the power lines based on the output points.

[0147] The two-dimensional processing module 12 is used to acquire line data based on the instruments installed at the output point and the instruments installed at the monitoring point, and to perform two-dimensional processing on the line data.

[0148] Spatial analysis module 13 is used to identify the two-dimensional processed line data and determine the spatial anomaly of each output point.

[0149] The time analysis module 14 is used to identify the line data of each output point and determine the time anomaly of each output point.

[0150] The energy storage node setting module 15 is used to calculate the spatial and temporal anomalies of all output points and determine the energy storage nodes.

[0151] Furthermore, the monitoring point determination module 11 includes:

[0152] The data query unit is used to obtain the distribution transformers installed in the target area (in this embodiment, the transformer substation), and to query the power lines in the target area (in this embodiment, the transformer substation) using the distribution transformers as line nodes;

[0153] The detection density calculation unit is used to obtain the output points on the power line and calculate the detection density at each location on the power line based on the output points.

[0154] The detection density update unit is used to select monitoring points based on the detection density and update the detection density synchronously; the loop execution unit is used to execute cyclically until the preset loop exit condition is met; the loop exit condition includes the detection density of all positions reaching the preset density value and the number of monitoring points reaching the preset number threshold.

[0155] Specifically, the two-dimensional processing module 12 includes:

[0156] The channel establishment unit is used to establish connection channels with instruments installed at output points and with instruments installed at monitoring points.

[0157] The channel application unit is used to obtain line data containing location tags based on the established connection channel;

[0158] The normalization processing unit is used to perform time-domain registration on line data, and to normalize line data containing location labels at the same time.

[0159] Data sorting unit, used to sort normalized data based on position labels.

[0160] In addition, the spatial analysis module 13 includes:

[0161] The two-dimensional data processing unit is used to perform two-dimensional Fourier transform on the two-dimensional processed line data to extract the spectrum and phase diagram;

[0162] The inverse transform unit is used to perform high-pass filtering of the spectrum graph at different sizes, and to perform inverse transform of the high-pass filtered spectrum graph based on the phase graph.

[0163] The location query unit is used to query the location of the retained data in the result obtained from the inverse transformation;

[0164] Anomaly calculation unit, used to determine the spatial anomaly of the location of the retained data based on the size of the high-pass filter;

[0165] Among them, the high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size is the radius of the circle; the spatial anomaly is proportional to the maximum radius corresponding to each location.

[0166] Furthermore, the time analysis module 14 includes:

[0167] The data difference calculation unit is used to statistically analyze the line data at each output point and calculate the data difference of the line data.

[0168] The data difference query unit is used to query the data difference within a preset backtracking time, starting from the current time.

[0169] The mean calculation unit is used to perform a preset ratio of extremum removal on the data differences within the backtracking time and calculate the standard deviation of the data differences after extremum removal.

[0170] An execution unit is used to determine the time anomaly of the output point based on a direct proportion to the standard deviation.

[0171] In addition, the energy storage node setting module 15 includes:

[0172] The data statistics unit is used to count the spatial and temporal anomalies of all output points and calculate the overall anomaly.

[0173] A prediction unit is used to determine a predicted energy storage area based on the comprehensive anomaly degree. The predicted energy storage area is a circular region, and the radius of the circular region is inversely proportional to the comprehensive anomaly degree. The predicted energy storage area is used to characterize the installation range of the energy storage node that supplies power to the output point.

[0174] The descending sorting unit is used to calculate the intersection of all predicted energy storage areas and sorts the intersection in descending order according to the number of predicted energy storage areas corresponding to each intersection.

[0175] The installation application unit is used to install energy storage nodes in the intersection in sequence until the number of energy storage nodes reaches a preset threshold.

[0176] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for configuring energy storage nodes based on spatiotemporal analysis, characterized in that, The method includes: The system acquires the distribution transformers and power lines based on the distribution transformers in the target area, obtains the output points of the power lines, and determines the monitoring points in the power lines based on the output points. The line data is acquired and processed into two dimensions; wherein the line data is acquired by instruments installed at the output point and the detection point. Based on the two-dimensional processed line data, the spatial anomaly of each output point is determined; whereby the spatial anomaly represents the difference between the data of any location at the current time and the data of its adjacent locations at the current time. Based on the line data at each output point, the time anomaly degree of each output point is determined; where the time anomaly degree represents the fluctuation of line data over a period of time. Statistically analyze the spatial and temporal anomalies of all output points to determine the energy storage nodes; The steps for determining energy storage nodes by statistically analyzing the spatial and temporal anomalies of all output points include: Statistically calculate the spatial and temporal anomalies of all output points, and then calculate the overall anomaly. The predicted energy storage area is determined based on the comprehensive anomaly degree; the predicted energy storage area is a circular region, and the radius of the circular region is inversely proportional to the comprehensive anomaly degree. The predicted energy storage area is used to characterize the installation range of the energy storage node that supplies power to the output point. Calculate the intersection of all predicted energy storage areas, and sort the intersections in descending order based on the number of predicted energy storage areas corresponding to each intersection. Install energy storage nodes sequentially within the intersection until the number of energy storage nodes reaches a preset threshold.

2. The energy storage node configuration method based on spatiotemporal analysis according to claim 1, characterized in that, The steps of acquiring the distribution transformers and power lines based on the distribution transformers in the target area, acquiring the output points of the power lines, and determining the monitoring points in the power lines based on the output points include: Obtain the distribution transformers installed in the target area, and use the distribution transformers as line nodes to query the power lines in the target area; Obtain the output points on the power line and calculate the detection density at each location on the power line based on the output points; Monitoring points are selected based on detection density, and the detection density is updated synchronously. The process is repeated until a preset loop exit condition is met; the loop exit condition includes the detection density at all locations reaching a preset density value and the number of monitoring points reaching a preset number threshold.

3. The energy storage node configuration method based on spatiotemporal analysis according to claim 1, characterized in that, The steps of acquiring line data and performing two-dimensional processing on the line data include: Establish connection channels with instruments installed at output points and with instruments installed at monitoring points; Obtain line data containing location tags based on the established connection channels; Perform time-domain registration on the line data and standardize the line data containing location labels at the same time. Data is arranged and normalized based on location labels.

4. The energy storage node configuration method based on spatiotemporal analysis according to claim 1, characterized in that, The steps for determining the spatial anomaly of each output point based on the two-dimensional processed line data include: Perform a two-dimensional Fourier transform on the line data after two-dimensional processing to extract the spectrum and phase diagram; High-pass filtering of different sizes is performed on the spectrum graph, and inverse transformation of the high-pass filtered spectrum graph is performed based on the phase graph. In the result obtained from the inverse transformation, find the location of the retained data; The spatial anomaly of the location of the retained data is determined based on the size of the high-pass filter. Among them, the high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size is the radius of the circle; the spatial anomaly is proportional to the maximum radius corresponding to each location.

5. The energy storage node configuration method based on spatiotemporal analysis according to claim 1, characterized in that, The step of determining the time anomaly degree of each output point based on the line data of each output point includes: Statistically analyze the line data for each output point and calculate the data difference of the line data; Starting from the current time, query the data difference within the preset backtracking time. Perform extreme value removal processing on the data differences within the backtracking time according to a preset ratio, and calculate the standard deviation of the data differences after extreme value removal processing; The time anomaly of the output point is determined based on the direct proportion to the standard deviation.

6. The energy storage node configuration method based on spatiotemporal analysis according to claim 1, characterized in that, During the installation of energy storage nodes, for each energy storage node installed, the predicted energy storage area corresponding to that energy storage node is removed once during the calculation of the intersection of all predicted energy storage areas.

7. An energy storage node configuration system based on spatiotemporal analysis, characterized in that, The system includes: The monitoring point determination module acquires the distribution transformers and power lines based on the distribution transformers in the target area, acquires the output points of the power lines, and determines the monitoring points in the power lines based on the output points. A two-dimensional processing module acquires line data and performs two-dimensional processing on the line data; wherein, the line data is acquired by instruments installed at the output point and the detection point; The spatial analysis module determines the spatial anomaly of each output point based on the two-dimensional processed line data; where spatial anomaly represents the difference between the data of any location at the current time and the data of its adjacent locations at the current time. The time analysis module determines the time anomaly degree of each output point based on the line data of each output point; where time anomaly degree represents the fluctuation of line data over a period of time. The energy storage node setting module is used to calculate the spatial and temporal anomalies of all output points and determine the energy storage nodes. The process of statistically analyzing the spatial and temporal anomalies of all output points to determine the energy storage nodes includes: Statistically calculate the spatial and temporal anomalies of all output points, and then calculate the overall anomaly. The predicted energy storage area is determined based on the comprehensive anomaly degree; the predicted energy storage area is a circular region, and the radius of the circular region is inversely proportional to the comprehensive anomaly degree. The predicted energy storage area is used to characterize the installation range of the energy storage node that supplies power to the output point. Calculate the intersection of all predicted energy storage areas, and sort the intersections in descending order based on the number of predicted energy storage areas corresponding to each intersection. Install energy storage nodes sequentially within the intersection until the number of energy storage nodes reaches a preset threshold.

8. The energy storage node configuration system based on spatiotemporal analysis according to claim 7, characterized in that, The monitoring point determination module includes: The data query unit is used to obtain the distribution transformers installed in the target area, and to query the power lines in the target area using the distribution transformers as line nodes; The detection density calculation unit is used to obtain the output points on the power line and calculate the detection density at each location on the power line based on the output points. The detection density update unit is used to select monitoring points based on the detection density and update the detection density synchronously. The loop execution unit is used to execute in a loop until the preset loop exit conditions are met; the loop exit conditions include the detection density of all positions reaching the preset density value and the number of monitoring points reaching the preset number threshold.

9. The energy storage node configuration system based on spatiotemporal analysis according to claim 7, characterized in that, The two-dimensional processing module includes: The channel establishment unit is used to establish connection channels with instruments installed at output points and with instruments installed at monitoring points. The channel application unit is used to obtain line data containing location tags based on the established connection channel; The normalization processing unit is used to perform time-domain registration on line data, and to normalize line data containing location labels at the same time. Data sorting unit, used to sort normalized data based on position labels.

10. The energy storage node configuration system based on spatiotemporal analysis according to claim 7, characterized in that, The spatial analysis module includes: The two-dimensional data processing unit is used to perform two-dimensional Fourier transform on the two-dimensional processed line data to extract the spectrum and phase diagram; The inverse transform unit is used to perform high-pass filtering of the spectrum graph at different sizes, and to perform inverse transform of the high-pass filtered spectrum graph based on the phase graph. The location query unit is used to query the location of the retained data in the result obtained from the inverse transformation; Anomaly calculation unit, used to determine the spatial anomaly of the location of the retained data based on the size of the high-pass filter; Among them, the high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size is the radius of the circle; the spatial anomaly is proportional to the maximum radius corresponding to each location.

11. The energy storage node configuration system based on spatiotemporal analysis according to claim 7, characterized in that, The time analysis module includes: The data difference calculation unit is used to statistically analyze the line data at each output point and calculate the data difference of the line data. The data difference query unit is used to query the data difference within a preset backtracking time, starting from the current time. The mean calculation unit is used to perform a preset ratio of extremum removal on the data differences within the backtracking time and calculate the standard deviation of the data differences after extremum removal. An execution unit is used to determine the time anomaly of the output point based on a direct proportion to the standard deviation.

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