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

Abstract

The invention relates to the technical field of power distribution network management, and particularly discloses an energy storage node configuration method and system based on space-time analysis, and the method comprises the steps: obtaining a distribution transformer of a target region and a power line based on the distribution transformer, obtaining an output point location of the power line, and determining a monitoring point location in the power line according to the output point location; acquiring line data, and performing two-dimensional processing on the line data; based on the line data after two-dimensional processing, determining the spatial anomaly degree of each output point location; based on the line data of each output point location, determining the time anomaly of each output point location; and carrying out statistics on space anomalies and time anomalies of all output point locations, and determining energy storage nodes. According to the method, space-time analysis is performed on the data of all the output point locations, some unstable output point locations are selected, some energy storage nodes are additionally arranged based on the output point locations and are used for supplying energy to the output point locations, and the stability of the whole power supply network is improved.

Description

[0001] This application is a divisional application of the invention application with the application date of December 4, 2024, the Chinese application number of 202411769130.1, and the invention name of "A micro-grid configuration method and system for weak areas in mountainous areas". TECHNICAL FIELD

[0002] The present application relates to the technical field of power distribution network management, and in particular to an energy storage node configuration method and system based on space-time analysis. BACKGROUND

[0003] "Area" is a term in the power system, which refers to an area served by a power transformer. Specifically, an area refers to a power supply range defined in a power distribution network, which is served by one or more distribution transformers. Each area usually serves a certain number of users, and users in the area share the power supply of one or more transformers.

[0004] In mountainous areas, the power transmission process is affected by the environment, and some power supply processes are very unstable. The area is relatively weak, which affects the life of users. How to improve the power supply stability of weak areas in mountainous areas is the technical problem that the present application technical solution wants to solve. SUMMARY

[0005] The present application aims to provide an energy storage node configuration method and system based on space-time analysis to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] Obtain the distribution transformers of the target area and the power lines based on the distribution transformers, obtain the output points of the power lines, and determine the monitoring points in the power lines according to the output points;

[0008] Obtain line data and perform two-dimensional processing on the line data; wherein the line data is obtained by instruments installed at the output points and the detection points;

[0009] Based on the two-dimensional processed line data, determine the spatial abnormality of each output point; wherein the spatial abnormality represents the difference between the data of any position at the current time and the data of the adjacent position at the current time;

[0010] Based on the line data of each output point, determine the time abnormality of each output point; wherein the time abnormality represents the fluctuation of the line data within a period of time;

[0011] Statistically analyze the spatial abnormality and the time abnormality of all output points to determine the energy storage node.

[0012] As a further scheme of the present application: the step of acquiring the distribution transformer of the target area and the power line based on the distribution transformer, acquiring the output point of the power line, and determining the monitoring point in the power line according to the output point includes:

[0013] Acquiring the distribution transformer installed in the target area, and querying the power line in the transformer area with the distribution transformer as the line node;

[0014] Acquiring the output point on the power line, and calculating the detection density at each position in the power line according to the output point;

[0015] Selecting the monitoring point based on the detection density, and synchronously updating the detection density;

[0016] Cyclically executing until a preset loop exit condition is met; the loop exit condition includes that the detection density of all positions reaches a preset density value and the number of monitoring points reaches a preset number threshold.

[0017] As a further scheme of the present application: 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 a preset correction coefficient, 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 the output point and the monitoring point.

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

[0020] Establishing a connection channel with the meter installed at the output point, and establishing a connection channel with the meter installed at the monitoring point;

[0021] Acquiring the line data containing the position label based on the established connection channel;

[0022] Performing time domain registration on the line data, and performing normalization processing on the line data containing the position label at the same time;

[0023] Arranging the normalized processed data based on the position label.

[0024] As a further scheme of the present application: the normalization processing process includes:

[0025] In the formula, y ′ is the normalized processed data, y is the data before normalization processing, y max is the maximum value of the line data, and y min is the minimum value of the line data.

[0026] As a further scheme of the present application: the step of determining the spatial abnormality degree of each output point based on the two-dimensional processed line data comprises:

[0027] Performing two-dimensional Fourier transform on the two-dimensional processed line data to extract a frequency spectrum and a phase diagram;

[0028] Performing high-pass filtering of different sizes on the frequency spectrum, and performing inverse transform on the high-pass filtered frequency spectrum based on the phase diagram;

[0029] In the result obtained by inverse transform, querying the position of the reserved data;

[0030] Determining the spatial abnormality degree of the position of the reserved data according to the size of the high-pass filtering;

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

[0032] As a further scheme of the present application: the step of determining the time abnormality degree of each output point based on the line data of each output point comprises:

[0033] Statistically processing the line data of each output point to obtain a data difference of the line data;

[0034] Taking the current time as the starting point, querying the data difference within a preset backtracking time;

[0035] Performing a preset proportion of extreme value processing on the data difference within the backtracking time, and calculating the standard deviation of the data difference after the extreme value processing;

[0036] Determining the time abnormality degree of the output point according to the direct proportion of the standard deviation.

[0037] As a further scheme of the present application: the step of determining the energy storage node based on the spatial abnormality degree and the time abnormality degree of all output points comprises:

[0038] Statistically processing the spatial abnormality degree and the time abnormality degree of all output points to calculate a comprehensive abnormality degree;

[0039] Determining a predicted energy storage area according to the comprehensive abnormality degree; the predicted energy storage area is a circular area, the radius of the circular area is inversely proportional to the comprehensive abnormality degree, and the predicted energy storage area is used to represent the installation range of the energy storage node for supplying power to the output point;

[0040] Calculating the intersection of all predicted energy storage areas, and arranging the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection;

[0041] Install the energy storage nodes in the intersection in sequence until the number of energy storage nodes reaches the preset number threshold.

[0042] As a further scheme of the present application: in the process of installing the energy storage nodes, the predicted energy storage area corresponding to each installed energy storage node is excluded in the process of calculating the intersection of all predicted energy storage areas.

[0043] The technical scheme of the present application also provides an energy storage node configuration system based on space-time analysis, which comprises:

[0044] A monitoring point determination module is configured to obtain distribution transformers in a target area and power lines based on the distribution transformers, obtain output points of the power lines, and determine monitoring points in the power lines according to the output points.

[0045] A two-dimensional processing module is configured to obtain line data and perform two-dimensional processing on the line data, wherein the line data is obtained by instruments installed at the output points and the monitoring points.

[0046] A space analysis module is configured to determine spatial abnormality degrees of the output points based on the two-dimensionally processed line data, wherein the spatial abnormality degree represents the difference between the data of any position at the current time and the data of the adjacent position at the current time.

[0047] A time analysis module is configured to determine time abnormality degrees of the output points based on the line data of each output point, wherein the time abnormality degree represents the fluctuation of the line data within a period of time.

[0048] As a further scheme of the present application: the monitoring point determination module comprises:

[0049] A data query unit is configured to obtain distribution transformers installed in the target area, and query power lines in the target area with the distribution transformers as line nodes.

[0050] A detection density calculation unit is configured to obtain output points on the power lines, and calculate detection densities at various positions in the power lines according to the output points.

[0051] A detection density updating unit is configured to select monitoring points based on the detection densities, and update the detection densities synchronously.

[0052] A loop execution unit is configured to execute loops until a preset loop exit condition is met, wherein the loop exit condition comprises that the detection densities of all positions reach a preset density value and the number of monitoring points reaches a preset number threshold.

[0053] As a further scheme of the present application: the two-dimensional processing module comprises:

[0054] The channel establishing unit is configured to establish a connection channel with the meter installed at the output point and establish a connection channel with the meter installed at the monitoring point.

[0055] The channel application unit is configured to acquire line data containing a position tag based on the established connection channel.

[0056] The normalization processing unit is configured to perform time domain registration on the line data, and perform normalization processing on the line data containing the position tag at the same time.

[0057] The data arrangement unit is configured to arrange the data after the normalization processing based on the position tag.

[0058] As a further scheme of the present application, the spatial analysis module comprises:

[0059] The two-dimensional data processing unit is configured to perform two-dimensional Fourier transform on the line data after the two-dimensional processing, and extract a frequency spectrum and a phase diagram.

[0060] The inverse transformation unit is configured to perform high-pass filtering of different sizes on the frequency spectrum, and perform inverse transformation on the high-pass filtered frequency spectrum based on the phase diagram.

[0061] The position query unit is configured to query the position of the reserved data in the result obtained by the inverse transformation.

[0062] The abnormality degree calculation unit is configured to determine the spatial abnormality degree of the position of the reserved data according to the size of the high-pass filtering.

[0063] The high-pass filtering scheme adopts a circular filtering scheme based on an origin, and the size adopts a radius of a circle; the spatial abnormality degree is proportional to the maximum radius corresponding to each position.

[0064] As a further scheme of the present application, the time analysis module comprises:

[0065] The data difference calculation unit is configured to calculate the data difference of the line data by counting the line data of each output point.

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

[0067] The mean value calculation unit is configured to perform a preset proportion of extreme value processing on the data difference within the backtracking time, and calculate the standard deviation of the data difference after the extreme value processing.

[0068] The execution unit is configured to determine the time abnormality degree of the output point according to the proportionality of the standard deviation.

[0069] As a further scheme of the present application, the energy storage node setting module comprises:

[0070] a data statistics unit configured to count spatial abnormality and time abnormality of all output points, and calculate a comprehensive abnormality;

[0071] a prediction unit configured to determine a predicted energy storage area according to the comprehensive abnormality, wherein the predicted energy storage area is a circular area, and a radius of the circular area is inversely proportional to the comprehensive abnormality, and the predicted energy storage area is used to represent an installation range of an energy storage node for supplying power to the output point;

[0072] a descending arrangement unit configured to calculate intersections of all predicted energy storage areas, and arrange the intersections in descending order according to a number of predicted energy storage areas corresponding to each intersection;

[0073] an installation application unit configured to install the energy storage nodes in the intersections in sequence until a number of the energy storage nodes reaches a preset number threshold.

[0074] Compared with the prior art, the present application has the following beneficial effects:

[0075] The present application performs space-time analysis on data of all output points, selects some unstable output points, and additionally arranges some energy storage nodes based on the output points to supply energy to the output points, thereby improving stability of the entire power supply network. BRIEF DESCRIPTION OF DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application.

[0077] Figure 1 A total flow chart of an energy storage node configuration method based on space-time analysis is shown.

[0078] Figure 2 A structure diagram of an energy storage node configuration system based on space-time analysis is shown. DETAILED DESCRIPTION

[0079] In order to make the technical problems to be solved by the present application, technical solutions and beneficial effects more clearly, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0080] Embodiment one:

[0081] Figure 1 The total flow chart of the energy storage node configuration method and system based on space-time analysis, in the embodiment of the present application, an energy storage node configuration method based on space-time analysis, the method comprises:

[0082] Step S100: Obtain the distribution transformer of the target area (in this embodiment, a transformer area) and the power line based on the distribution transformer, obtain the output point of the power line, and determine the monitoring point in the power line according to the output point;

[0083] Step S200: Obtain the line data according to the meter installed at the output point and the meter installed at the monitoring point, and perform two-dimensional processing on the line data;

[0084] Step S300: Identify the line data after two-dimensional processing, and determine the spatial abnormality degree of each output point;

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

[0086] Step S500: Count the spatial abnormality degree and the time abnormality degree of all output points, and determine the energy storage node.

[0087] "Transformer area" is a term in the power system, which refers to an area powered by a power transformer. Specifically, a transformer area refers to a power supply range defined in a power distribution network that is powered by one or more distribution transformers. Each transformer area usually serves a certain number of users, and the users in the transformer area share the power supply of one or more transformers.

[0088] The distribution transformer of each transformer area and the power line based on the distribution transformer are existing data, which can be directly read. On this basis, the power line is connected to which power consumption unit is obtained, which is called the output point. The output point will be equipped with a meter. On this basis, based on the distribution of the output point, some monitoring points are supplemented in the power line, which can make the line data acquisition process more comprehensive.

[0089] The line data is obtained according to the meter installed at the output point and the meter installed at the monitoring point. Each line data is the data at a monitoring point or an output point, records its position, sorts the line data according to the position, and then performs two-dimensional processing on the line data. After two-dimensional processing, the data after two-dimensional processing is identified, which can be positioned to some relatively abnormal positions, which are called spatial abnormality degrees. The spatial abnormality degree represents the difference between the data of any position at the current time and the data of the adjacent position at the current time. Generally, if there is a branch between adjacent positions, the difference between the line data will be larger.

[0090] For each installation position of the meter, the line data obtained is arranged in time sequence, and the fluctuation of the line data is analyzed, and the time abnormality degree can be obtained. The time abnormality degree represents the fluctuation of the line data in a period of time.

[0091] Finally, according to the spatial anomaly degree and the time anomaly degree, some output points are selected, and some energy storage nodes are arranged based on the output points, so as to compensate the electric energy and improve the stability in the use process.

[0092] The actual function of the technical scheme of the application is to select some unstable output points from multiple output points, arrange some energy storage nodes based on the output points, supply energy to the output points, and improve the stability of the entire power supply network.

[0093] Embodiment two:

[0094] Regarding step S100, the power distribution transformer of the target area (the district in this embodiment) is acquired, and the power lines based on the power distribution transformer are acquired, the output points of the power lines are acquired, and the monitoring points in the power lines are determined according to the output points.

[0095] The power distribution transformer installed in the target area (the district in this embodiment) is acquired, and the power lines in the target area (the district in this embodiment) are queried with the power distribution transformer as a line node.

[0096] The output points on the power lines are acquired, and the detection densities at various positions in the power lines are calculated according to the output points.

[0097] The monitoring points are selected based on the detection densities, and the detection densities are synchronously updated.

[0098] The cycle is executed until a preset cycle exit condition is met; the cycle exit condition includes that the detection densities of all positions reach a preset density value and the number of monitoring points reaches a preset number threshold.

[0099] The above content specifically limits the selection process of the monitoring points, and the target to be analyzed by the application is the output points. The instrument is installed at the output points, and the electricity data is known. If the monitoring points are not installed, the output points are directly analyzed, which is also feasible, but the comprehensiveness of the data decreases slightly. Therefore, the application also provides a monitoring point selection scheme, which aims to improve the comprehensiveness of the data acquisition process.

[0100] The power distribution transformer installed in the target area (the district in this embodiment) is acquired, and the power lines in the target area (the district in this embodiment) are queried with the power distribution transformer as a line node. The power distribution transformer is an output power device, and the power lines are connected to the electricity consuming units. The output points on the power lines are acquired, and the detection densities at various positions in the power lines are calculated according to the output points. The detection density represents how many detection resources there are at a position.

[0101] Comparing the detection density at each position, installing a monitoring point at the lowest detection density, at this time, the detection density of all positions is updated, then installing a monitoring point at the lowest detection density, and repeating until the detection density of all positions reaches the preset density value or the number of monitoring points reaches the preset number threshold.

[0102] Further, the detection density calculation process includes:

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

[0104] The x position in the detection density calculation process needs to be explained. The present application considers that the line is one-dimensional, so the line needs to be simply arranged as a line in a preset order in advance. After selecting an origin on the line, all positions can be represented by the parameter x. Each point with a meter will affect the density at the x position. The farther the distance, the smaller the influence. For any position, superimpose the influence of all points with meters at the position to obtain the final detection density.

[0105] Embodiment three:

[0106] Step S200, the step of obtaining line data according to the meters installed at the output points and the meters installed at the monitoring points and performing two-dimensional processing on the line data includes:

[0107] Establishing a connection channel with the meters installed at the output points and establishing a connection channel with the meters installed at the monitoring points;

[0108] Obtaining line data containing position labels based on the established connection channel;

[0109] Performing time domain registration on the line data, and performing standardized processing on the line data containing position labels at the same time;

[0110] Arranging the data after the standardized processing based on the position labels.

[0111] The existing instrument has a data transmission function, a connection channel with the instrument is established, and line data containing a position tag is obtained based on the established connection channel. In the technical scheme of the application, the data acquisition frequency of all instruments is the same, but due to the time required in the transmission process, the line data of different instruments obtained will be offset in time. For this case, line data with a small enough time difference is regarded as data at the same time, that is, time domain registration. For line data at the same time, the line data is 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, in order to facilitate processing, the application also performs normalization processing on the line data in the process of arranging the data based on the position tag and normalizing the processed data, that is, the line data is processed by de-dimensioning.

[0113] Further, the normalization processing process includes:

[0114] In the formula, y ′ is the normalized data, y is the data before normalization, y max is the maximum value of the line data, y min is the minimum value of the line data.

[0115] The normalization process itself is very simple, and it is worth mentioning that the normalized data will fall within the range of 0 to 255, which is actually the range of gray values, so that the two-dimensional processed data can be connected to most image processing algorithms for calculating spatial anomaly degree.

[0116] Embodiment four:

[0117] Regarding step S300, the step of identifying the two-dimensional processed line data and determining the spatial anomaly degree of each output point includes:

[0118] Performing two-dimensional Fourier transform on the two-dimensional processed line data to extract a frequency spectrum and a phase diagram;

[0119] Performing high-pass filtering of different sizes on the frequency spectrum, and performing inverse transform on the high-pass filtered frequency spectrum based on the phase diagram;

[0120] In the result obtained by inverse transform, the position of the reserved data is queried;

[0121] The spatial anomaly degree of the position of the reserved data is determined according to the size of the high-pass filtering;

[0122] The high-pass filtering scheme adopts a circular filtering scheme based on the origin, and the size adopts a circular radius; the spatial abnormality degree is proportional to the maximum radius corresponding to each position.

[0123] The above content specifically limits the calculation process of the spatial abnormality degree. The line data after two-dimensional processing can be analogized as an image. The two-dimensional Fourier transform of the line data after two-dimensional processing can obtain a frequency spectrum and a phase diagram. The frequency spectrum reflects the change of each data after two-dimensional processing. The high-frequency part corresponds to the part with rapid change. The high-pass filtering of the frequency spectrum, that is, the removal of the low-frequency part (around the origin in the frequency domain diagram), can retain the positions with rapid change.

[0124] In the process of removing the low-frequency part, how much area is removed is the size in the above content. The larger the size, the more rapid the change of the retained position, and the higher the spatial abnormality degree. Since the same position may be retained in the removal process of multiple sizes, the maximum size is selected to determine the spatial abnormality degree in direct proportion to the maximum size. The function of the direct proportion can be a composite function based on an exponential function, a composite function based on a logarithmic function, or a composite function based on a power function. The specific parameters are determined by the staff according to the situation.

[0125] Embodiment five:

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

[0127] Statistically analyze the line data of each output point to obtain the data difference of the line data;

[0128] Taking the current time as the starting point, query the data difference within the preset backtracking time;

[0129] Perform a preset proportion of extreme value processing on the data difference within the backtracking time, and calculate the standard deviation of the data difference after the extreme value processing;

[0130] Determine the time abnormality degree of the output point in direct proportion to the standard deviation.

[0131] In an example of the technical scheme of the present application, a time abnormality degree calculation process is described, which is actually a fluctuation identification process, line data of each output point is counted, and a data difference of the line data is calculated, the data difference is a derivative of discrete data, and reflects a change; data difference within a preset backtracking time from the current time is queried, the backtracking time is generally one day; the data difference within the backtracking time is subjected to a preset proportion of extreme value processing, a standard deviation of the data difference after the extreme value processing is calculated, the extreme value processing is a common description, that is, how many maximum values are removed, and how many minimum values are removed, and the purpose of the extreme value processing is to prevent misjudgment (due to a sharp fluctuation caused by noise); the data difference after the extreme value processing is analyzed, a standard deviation can be calculated, and the time abnormality degree of the output point can be determined according to the direct proportion of the standard deviation, that is, the greater the standard deviation, the greater the fluctuation, and the greater the time abnormality degree.

[0132] Further, the step of counting the spatial abnormality degree and the time abnormality degree of all output points and determining the energy storage node includes:

[0133] Counting the spatial abnormality degree and the time abnormality degree of all output points and calculating a comprehensive abnormality degree;

[0134] Determining a predicted energy storage area according to the comprehensive abnormality degree; the predicted energy storage area is a circular area, a radius of the circular area is inversely proportional to the comprehensive abnormality degree, and the predicted energy storage area is used to represent an installation range of an energy storage node for supplying power to the output point;

[0135] Calculating intersections of all predicted energy storage areas, and arranging the intersections in descending order according to a number of predicted energy storage areas corresponding to each intersection;

[0136] Installing the energy storage node in the intersections in sequence until a number of energy storage nodes reaches a preset number threshold.

[0137] In an example of the technical scheme of the present application, the spatial abnormality degree and the time abnormality degree of all output points are counted, a comprehensive abnormality degree is calculated (which can be added according to a preset weight), a radius is determined according to the comprehensive abnormality degree, a circular area with the output point as the center is created, and is called a predicted energy storage area; the greater the comprehensive abnormality degree, the smaller the radius, and the greater the range in which an energy supply point needs to be set when assisting the output point in energy supply.

[0138] After the predicted energy storage areas of all output points are determined, intersections of all predicted energy storage areas are calculated, a number of predicted energy storage areas corresponding to each intersection is different, and the more corresponding, the greater the significance of setting the energy storage node at the intersection; the intersections are arranged in descending order according to the number of predicted energy storage areas corresponding to each intersection, and then the intersections are selected in sequence, and the energy storage node can be installed in the intersections.

[0139] It should be noted that the predicted energy storage area itself is a range in space, and the intersection is also a range. The energy storage node can be set in the intersection by random setting, as long as the energy storage node is in the intersection.

[0140] Embodiment six:

[0141] In the process of installing the energy storage node, each time an energy storage node is installed, the predicted energy storage area corresponding to the energy storage node is excluded once in the process of calculating the intersection of all predicted energy storage areas.

[0142] The above content adds a simplified scheme, which deletes the corresponding predicted energy storage area each time an energy storage node is set, indicating that the corresponding output node has an auxiliary energy supply device. This can prevent the concentration of energy storage nodes, that is, one output node can be powered by multiple energy storage nodes.

[0143] In fact, both schemes provided by the present application are feasible, which affects the number of energy storage node settings and the cost problem.

[0144] Embodiment seven:

[0145] Figure 2 The structure diagram of the energy storage node configuration system based on space-time analysis is shown. In a preferred embodiment of the technical scheme of the present application, an energy storage node configuration system based on space-time analysis is also provided. The system 10 comprises:

[0146] A monitoring point determination module 11 is configured to obtain the distribution transformer of a target area (in this embodiment, a transformer area) and the power line based on the distribution transformer, obtain the output point of the power line, and determine the monitoring point in the power line according to the output point.

[0147] A two-dimensional processing module 12 is configured to obtain line data according to the instrument installed at the output point and the instrument installed at the monitoring point, and perform two-dimensional processing on the line data.

[0148] A space analysis module 13 is configured to identify the line data after two-dimensional processing, and determine the spatial abnormality degree of each output point.

[0149] A time analysis module 14 is configured to identify the line data of each output point, and determine the time abnormality degree of each output point.

[0150] An energy storage node setting module 15 is configured to count the spatial abnormality degree and the time abnormality degree of all output points, and determine the energy storage node.

[0151] Further, the monitoring point determination module 11 comprises:

[0152] The data query unit is configured to acquire distribution transformers installed in a target area (in this embodiment, a transformer district), and query power lines in the target area (in this embodiment, the transformer district) by taking the distribution transformers as line nodes;

[0153] The detection density calculation unit is configured to acquire output points on the power lines, and calculate detection densities at various positions on the power lines according to the output points;

[0154] The detection density updating unit is configured to select monitoring points based on the detection densities, and update the detection densities synchronously; and the loop execution unit is configured to execute the loop until a preset loop exit condition is met; the loop exit condition includes that the detection densities at all positions reach a preset density value and the number of monitoring points reaches a preset number threshold.

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

[0156] The channel establishing unit is configured to establish a connection channel with the meter installed at the output point, and establish a connection channel with the meter installed at the monitoring point;

[0157] The channel application unit is configured to acquire line data containing a position label based on the established connection channel;

[0158] The normalization processing unit is configured to perform time domain registration on the line data, and perform normalization processing on the line data containing the position label at the same time point;

[0159] The data arrangement unit is configured to arrange the data after the normalization processing based on the position label.

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

[0161] The two-dimensional data processing unit is configured to perform two-dimensional Fourier transform on the line data after the two-dimensional processing, and extract a frequency spectrum and a phase diagram;

[0162] The inverse transformation unit is configured to perform high-pass filtering of different sizes on the frequency spectrum, and perform inverse transformation on the frequency spectrum after the high-pass filtering based on the phase diagram;

[0163] The position query unit is configured to query the position of the retained data in the result obtained by the inverse transformation;

[0164] The abnormality calculation unit is configured to determine a spatial abnormality of the position of the retained data according to the size of the high-pass filtering;

[0165] The high-pass filtering scheme adopts a circular filtering scheme based on an origin, and the size adopts a radius of a circle; the spatial abnormality is proportional to a maximum radius corresponding to each position.

[0166] Further, the time analysis module 14 comprises:

[0167] a data difference obtaining unit configured to count line data of each output point and obtain data difference of the line data;

[0168] a data difference querying unit configured to query data difference within a preset backtracking time from a current time point;

[0169] a mean value calculation unit configured to perform a preset proportion of extreme value processing on the data difference within the backtracking time and calculate standard deviation of the data difference after the extreme value processing;

[0170] an execution unit configured to determine time abnormality degree of the output point according to a direct ratio of the standard deviation.

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

[0172] a data counting unit configured to count spatial abnormality degree and time abnormality degree of all output points and calculate comprehensive abnormality degree;

[0173] a prediction unit configured to determine a predicted energy storage area according to the comprehensive abnormality degree; the predicted energy storage area is a circular area, a radius of the circular area is inversely proportional to the comprehensive abnormality degree, and the predicted energy storage area is used to represent an installation range of an energy storage node for supplying power to the output point;

[0174] a descending arrangement unit configured to calculate intersections of all predicted energy storage areas and arrange the intersections in descending order according to a number of the predicted energy storage areas corresponding to each intersection;

[0175] an installation application unit configured to install the energy storage nodes in the intersections in sequence until a number of the energy storage nodes reaches a preset number threshold.

[0176] The above is only a preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation according to the content of the specification and the drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

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.

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, 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.

7. The energy storage node configuration method based on spatiotemporal analysis according to claim 6, 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.

8. 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.

9. The energy storage node configuration system based on spatiotemporal analysis according to claim 8, 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.

10. The energy storage node configuration system based on spatiotemporal analysis according to claim 8, 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.

11. The energy storage node configuration system based on spatiotemporal analysis according to claim 8, 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.

12. The energy storage node configuration system based on spatiotemporal analysis according to claim 8, 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.

Images