A microgrid configuration method and system for weak substations in mountainous areas

Abstract

The present invention relates to the field of distribution network management technology, and specifically discloses a microgrid configuration method and system for weak substations in mountainous areas. The method includes obtaining line data based on instruments installed at output points and instruments installed at monitoring points, and performing two-dimensional processing on the line data; identifying the two-dimensionally processed line data to determine the spatial anomaly of each output point; identifying the line data of each output point to determine the temporal anomaly of each output point; and calculating the spatial anomaly and temporal anomaly of all output points to determine energy storage nodes. The present invention performs spatiotemporal analysis on the data of all output points, selects some unstable output points, and arranges some additional energy storage nodes based on these output points to supply energy to these output points, thereby improving the stability of the entire power supply network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network management, and in particular to a microgrid configuration method and system for weak substations in mountainous areas. Background Art

[0002] "Substation" is a term used in the power system to refer to an area supplied by a power transformer. Specifically, a substation is a defined area within the power distribution network, supplied by one or more distribution transformers. Each substation typically serves a certain number of customers, who share the power supply of one or more transformers.

[0003] In mountainous areas, the power transmission process will be affected by the environment, some power supply processes will be very unstable, and the substations are relatively weak, affecting the lives of users. How to improve the power supply stability of weak substations in mountainous areas is the technical problem that the technical solution of the present invention aims to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide a microgrid configuration method and system for weak substations in mountainous areas to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A microgrid configuration method and system for weak substations in mountainous areas, the method comprising:

[0007] Obtain the distribution transformer of the substation 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;

[0008] 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;

[0009] Identify the line data after two-dimensional processing and determine the spatial abnormality of each output point;

[0010] Identify the line data of each output point and determine the time anomaly of each output point;

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

[0012] As a further solution of the present invention, the steps of obtaining the distribution transformer of the substation and the power line based on the distribution transformer, obtaining the output point of the power line, and determining the monitoring point in the power line according to the output point include:

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

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

[0015] Select monitoring points based on detection density and update detection density simultaneously;

[0016] The loop is executed 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 solution of the present invention: the calculation process of the detection density includes:

[0018] Where 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 the x-position, and N is the total number of points; the points include output points and monitoring points.

[0019] As a further solution of the present invention: the step of obtaining line data based on the instrument installed at the output point and the instrument installed at the monitoring point, and performing two-dimensional processing on the line data includes:

[0020] Establish a connection channel with the instrument installed at the output point, and establish a connection channel with the instrument installed at the monitoring point;

[0021] Obtaining line data containing location tags based on the established connection channel;

[0022] Perform time domain registration on the line data and normalize the line data with location tags at the same time;

[0023] Arrange the normalized data based on position labels.

[0024] As a further solution of the present invention: the process of the normalization processing includes:

[0025] Where y ′ is the data after normalization, y is the data before normalization, and y max is the maximum value of the line data, y min This is the minimum value of the line data.

[0026] As a further solution of the present invention, the step of identifying the two-dimensionally processed line data and determining the spatial abnormality of each output point includes:

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

[0028] Perform high-pass filtering of different sizes on the spectrum graph, and perform inverse transformation on the high-pass filtered spectrum graph based on the phase image;

[0029] In the result obtained by the inverse transformation, query the location of the retained data;

[0030] Determine the spatial abnormality of the location of the retained data according to 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 adopts the radius of the circle; the spatial anomaly degree is proportional to the maximum radius corresponding to each position.

[0032] As a further solution of the present invention: the step of identifying the line data of each output point and determining the time anomaly degree of each output point includes:

[0033] Count the line data of each output point and calculate the data difference of the line data;

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

[0035] Perform a preset ratio of de-extreme value processing on the data differences within the retrospective time, and calculate the standard deviation of the data differences after de-extreme value processing;

[0036] The temporal anomaly degree of the output point is determined according to the direct proportion of the standard deviation.

[0037] As a further solution of the present invention, the step of counting the spatial anomaly and temporal anomaly of all output points and determining the energy storage node includes:

[0038] Count the spatial anomaly and temporal anomaly of all output points and calculate the comprehensive anomaly;

[0039] Determine a predicted energy storage area based on the comprehensive abnormality; the predicted energy storage area is a circular area, the radius of which is inversely proportional to the comprehensive abnormality, and the predicted energy storage area is used to represent 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 according to the number of predicted energy storage areas corresponding to each intersection;

[0041] Energy storage nodes are installed in the intersection in sequence until the number of energy storage nodes reaches a preset threshold.

[0042] As a further solution 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 the energy storage node is eliminated once in the process of calculating the intersection of all predicted energy storage areas.

[0043] The technical solution of the present invention also provides a microgrid configuration system for weak substations in mountainous areas, the system comprising:

[0044] A monitoring point determination module is used to obtain the distribution transformer of the substation 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;

[0045] A two-dimensional processing module is used to 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;

[0046] The spatial analysis module is used to identify the line data after two-dimensional processing and determine the spatial abnormality of each output point;

[0047] The time analysis module is used to identify the line data of each output point and determine the time anomaly of each output point;

[0048] The energy storage node setting module is used to count the spatial and temporal anomalies of all output points and determine the energy storage nodes.

[0049] As a further solution of the present invention: the monitoring point determination module includes:

[0050] A data query unit is used to obtain the distribution transformers installed in the substation area and query the power lines in the substation area using the distribution transformers as line nodes;

[0051] A detection density calculation unit is used to obtain output points on the power line and calculate the detection density at each position in the power line according to the output points;

[0052] A detection density update unit, used to select monitoring points based on the detection density and synchronously update the detection density;

[0053] The loop execution unit is used for loop execution 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.

[0054] As a further solution of the present invention: the two-dimensional processing module includes:

[0055] A channel establishing unit is used to establish a connection channel with an instrument installed at an output point and a connection channel with an instrument installed at a monitoring point;

[0056] A channel application unit is used to obtain line data containing location tags based on the established connection channel;

[0057] A normalization processing unit is used to perform time domain registration on the line data and normalize the line data with location tags at the same time;

[0058] The data arrangement unit is used to arrange the normalized data based on the position labels.

[0059] As a further solution of the present invention: the spatial analysis module includes:

[0060] A two-dimensional data processing unit, used to perform a two-dimensional Fourier transform on the line data after two-dimensional processing, and extract a spectrum diagram and a phase diagram;

[0061] an inverse transformation unit, configured to perform high-pass filtering of different sizes on the spectrum graph, and perform inverse transformation on the spectrum graph after high-pass filtering based on the phase graph;

[0062] A position query unit, used to query the position of the retained data in the result obtained by the inverse transformation;

[0063] an abnormality calculation unit, for determining the spatial abnormality of the position of the retained data according to the size of the high-pass filter;

[0064] 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 anomaly degree is proportional to the maximum radius corresponding to each position.

[0065] As a further solution of the present invention: the time analysis module includes:

[0066] The data difference obtaining unit is used to count the line data of each output point and obtain the data difference of the line data;

[0067] A data difference query unit is used to query the data difference within a preset backtracking time starting from the current time;

[0068] A mean calculation unit is used to perform a de-extreme value process on the data differences within the retrospective time at a preset ratio, and calculate the standard deviation of the data differences after the de-extreme value process;

[0069] The execution unit is used to determine the temporal anomaly degree of the output point according to the direct proportion of the standard deviation.

[0070] As a further solution of the present invention: the energy storage node setting module includes:

[0071] The data statistics unit is used to count the spatial anomaly and temporal anomaly of all output points and calculate the comprehensive anomaly;

[0072] a prediction unit, configured to determine a predicted energy storage area based on the comprehensive abnormality; the predicted energy storage area being a circular area whose radius is inversely proportional to the comprehensive abnormality, and the predicted energy storage area being used to characterize the installation range of the energy storage node supplying power to the output point;

[0073] A descending order arrangement unit is used to calculate the intersection of all predicted energy storage areas and arrange the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection;

[0074] 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 number threshold.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] The present invention performs spatiotemporal analysis on the data of all output points, selects some unstable output points, and arranges some additional energy storage nodes based on these output points to supply energy to these output points, thereby improving the stability of the entire power supply network. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0078] Figure 1 The overall flow chart of the microgrid configuration method for weak substations in mountainous areas is shown.

[0079] Figure 2 The structural diagram of the microgrid configuration system for weak substations in mountainous areas is shown. DETAILED DESCRIPTION

[0080] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] Example 1:

[0082] Figure 1 The following is a general flow chart of a microgrid configuration method and system for weak station areas in mountainous areas. In an embodiment of the present invention, a microgrid configuration method for weak station areas in mountainous areas includes:

[0083] Step S100: obtaining a distribution transformer in the substation area and a power line based on the distribution transformer, obtaining an output point of the power line, and determining a monitoring point in the power line according to the output point;

[0084] Step S200: acquiring line data according to the instrument installed at the output point and the instrument installed at the monitoring point, and performing two-dimensional processing on the line data;

[0085] Step S300: Identify the two-dimensionally processed line data and determine the spatial abnormality of each output point;

[0086] Step S400: Identify the line data of each output point and determine the time anomaly of each output point;

[0087] Step S500: Count the spatial anomaly and temporal anomaly of all output points to determine the energy storage nodes.

[0088] "Substation" is a term used in the power system to refer to an area supplied by a power transformer. Specifically, a substation is a defined area within the power distribution network, supplied by one or more distribution transformers. Each substation typically serves a certain number of customers, who share the power supply of one or more transformers.

[0089] The distribution transformers in each substation and the power lines based on the distribution transformers already have data and can be read directly. On this basis, we can obtain which electricity-consuming units the power lines are connected to, which are called output points. There must be an electricity meter at the output point. On this basis, based on the distribution of output points, some monitoring points are added to the power lines to make the line data acquisition process more comprehensive.

[0090] Line data is acquired using instruments installed at output points and monitoring points. Each line data point represents data from a monitoring point or an output point. The location of each line data point is recorded and sorted based on the location. The line data is then converted to two dimensions. After the conversion, the converted data is identified, and unusual locations can be located, which is known as the spatial anomaly degree. The spatial anomaly degree represents the difference between the data at any location at the current moment and the data at adjacent locations at the current moment. Generally, if there are branches between adjacent locations, the difference between their line data will be relatively large.

[0091] For each meter installation location, the acquired line data is arranged in chronological order, and the fluctuation of the line data is analyzed to obtain a temporal anomaly degree; the temporal anomaly degree indicates the fluctuation of the line data over a period of time.

[0092] Finally, some output points are selected according to the spatial anomaly and temporal anomaly, and some energy storage nodes are set based on the output points to compensate for their electric energy and improve the stability of their use process.

[0093] The actual function of the technical solution of the present invention is to select some unstable output points from multiple output points, and arrange some additional energy storage nodes based on these output points to supply energy to these output points, thereby improving the stability of the entire power supply network.

[0094] Example 2:

[0095] Regarding step S100, the steps of obtaining the distribution transformer of the substation and the power line based on the distribution transformer, obtaining the output point of the power line, and determining the monitoring point in the power line according to the output point include:

[0096] Obtain the distribution transformers installed in the substation area, and use the distribution transformers as line nodes to query the power lines within the substation area;

[0097] Obtain output points on the power line, and calculate the detection density at each position in the power line based on the output points;

[0098] Select monitoring points based on detection density and update detection density simultaneously;

[0099] The loop is executed 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.

[0100] The above content specifically limits the selection process of monitoring points. The ultimate target to be analyzed in this application is the output point, and the instrument is installed at the output point, and its electricity consumption data is known. If the monitoring point is not installed, it is feasible to directly analyze the output point, but the comprehensiveness of the data will be slightly reduced; therefore, this application also provides a monitoring point selection scheme, which aims to improve the comprehensiveness of the data acquisition process.

[0101] Obtain the distribution transformers installed in the substation, and use the distribution transformers as line nodes to query the power lines within the substation. The distribution transformers are devices that output electricity. The power lines are connected to electricity users. Obtain the output points on the power lines, and calculate the detection density at each location in the power lines based on the output points. The detection density indicates how many detection resources are available at a certain location.

[0102] Compare the detection density at each location and install a monitoring point at the lowest detection density. At this time, the detection density of all locations is updated accordingly. Then install another monitoring point at the lowest detection density. Repeat the process until the detection density of all locations reaches the preset density value or the number of monitoring points reaches the preset threshold.

[0103] Furthermore, the calculation process of the detection density includes:

[0104] Where 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 the x-position, and N is the total number of points; the points include output points and monitoring points.

[0105] The x position in the calculation process of the detection density needs to be explained. This application considers the line to be one-dimensional. Therefore, the line needs to be simplified into a line in advance according to the preset order. After selecting an origin on the line, all positions can be represented by the parameter x; each point with an instrument will affect the density at the x position. The farther the distance, the smaller the impact. For any position, the final detection density can be obtained by superimposing the influence of all points with instruments at that position.

[0106] Example 3:

[0107] Step S200, the step of acquiring line data based on the instrument installed at the output point and the instrument installed at the monitoring point, and performing two-dimensional processing on the line data includes:

[0108] Establish a connection channel with the instrument installed at the output point, and establish a connection channel with the instrument installed at the monitoring point;

[0109] Obtaining line data containing location tags based on the established connection channel;

[0110] Perform time domain registration on the line data and normalize the line data with location tags at the same time;

[0111] Arrange the normalized data based on position labels.

[0112] All existing instruments have data transmission functions. A connection channel is established with the instrument, and line data containing location tags is obtained based on the established connection channel. In the technical solution of the present invention, the data acquisition frequency of all instruments is the same. However, since the transmission process itself takes time, the line data obtained from different instruments will have a time offset. In this case, the line data with a sufficiently small time difference needs to be regarded as data at the same time, that is, time domain alignment. For the line data at the same time, 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.

[0113] In addition, for ease of processing, in the process of arranging the normalized data based on the position labels, the present application also normalizes the line data, that is, performs dimensionless processing on the line data.

[0114] Furthermore, the process of the normalization includes:

[0115] Where y ′ is the data after normalization, y is the data before normalization, and y max is the maximum value of the line data, y min This is the minimum value of the line data.

[0116] The normalization process itself is very simple. It is worth mentioning that the normalized data will fall into the range of 0 to 255, which is actually the range of grayscale values. This makes the two-dimensional data compatible with most image processing algorithms for calculating spatial anomaly.

[0117] Example 4:

[0118] Regarding step S300, the step of identifying the two-dimensionally processed line data and determining the spatial abnormality of each output point includes:

[0119] Perform two-dimensional Fourier transform on the two-dimensional line data to extract the spectrum and phase diagram;

[0120] Perform high-pass filtering of different sizes on the spectrum graph, and perform inverse transformation on the high-pass filtered spectrum graph based on the phase image;

[0121] In the result obtained by the inverse transformation, query the location of the retained data;

[0122] Determine the spatial abnormality of the location of the retained data according to the size of the high-pass filter;

[0123] 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 anomaly degree is proportional to the maximum radius corresponding to each position.

[0124] The above content specifically limits the calculation process of spatial anomaly. The line data after two-dimensional processing can be compared to an image. A two-dimensional Fourier transform is performed on the line data after two-dimensional processing to obtain a spectrum diagram and a phase diagram. The spectrum diagram reflects the changes in each data after two-dimensional processing. The high-frequency part corresponds to the part with drastic changes. The spectrum diagram is high-pass filtered, that is, the low-frequency part (around the origin in the frequency domain diagram) is eliminated, and the locations with more drastic changes can be retained.

[0125] Among them, in the process of removing the low-frequency part, the size of the removed area is the size of the above-mentioned content. The larger the size, the more drastic the change in the retained position, and accordingly, the higher the spatial anomaly. Since the same position may be retained in the removal process of multiple sizes, the maximum size is selected, and the spatial anomaly is determined in direct proportion to the maximum size. The proportional selection function 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.

[0126] Embodiment 5:

[0127] Regarding step S400, the step of identifying the line data of each output point and determining the time anomaly of each output point includes:

[0128] Count the line data of each output point and calculate the data difference of the line data;

[0129] Starting from the current moment, query the data difference within the preset backtracking time;

[0130] Perform a preset ratio of de-extreme value processing on the data differences within the retrospective time, and calculate the standard deviation of the data differences after de-extreme value processing;

[0131] The temporal anomaly degree of the output point is determined according to the direct proportion of the standard deviation.

[0132] In an example of the technical solution of the present invention, a calculation process of the temporal anomaly degree is described. The calculation process of the temporal anomaly degree is actually a fluctuation identification process. The line data of each output point is counted, and the data difference of the line data is obtained. The data difference is the derivative of discrete data and reflects the change situation. Taking the current moment as the starting point, the data difference within a preset lookback time is queried. The lookback time is generally one day. The data difference within the lookback time is subjected to a preset proportion of de-extreme value processing, and the standard deviation of the data difference after de-extreme value processing is calculated. The popular description of the de-extreme value processing is to remove the number of maximum values ​​and the number of minimum values. The purpose of the de-extreme value processing is to prevent misjudgment (due to the violent fluctuation caused by noise). The data difference after de-extreme value processing is analyzed to calculate the standard deviation. The temporal anomaly degree of the output point can be determined based on the direct proportion of the standard deviation. That is, the larger the standard deviation, the greater the fluctuation and the greater the temporal anomaly degree.

[0133] Furthermore, the step of counting the spatial anomaly and temporal anomaly of all output points to determine the energy storage node includes:

[0134] Count the spatial anomaly and temporal anomaly of all output points and calculate the comprehensive anomaly;

[0135] Determine a predicted energy storage area based on the comprehensive abnormality; the predicted energy storage area is a circular area, the radius of which is inversely proportional to the comprehensive abnormality, and the predicted energy storage area is used to represent the installation range of the energy storage node that supplies power to the output point;

[0136] Calculate the intersection of all predicted energy storage areas and sort the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection;

[0137] Energy storage nodes are installed in the intersection in sequence until the number of energy storage nodes reaches a preset threshold.

[0138] In one example of the technical solution of the present invention, the spatial and temporal anomalies of all output points are counted, and the comprehensive anomaly is calculated (they can be summed according to preset weights). A radius is determined based on the comprehensive anomaly, and a circular area with the output point as the center is created, which is called the predicted energy storage area. The larger the comprehensive anomaly, the smaller the radius, which indicates the range within which the energy supply point needs to be set when auxiliary energy is supplied to the output point.

[0139] After determining the predicted energy storage areas for all output points, calculate the intersection of all predicted energy storage areas. Each intersection corresponds to a different number of predicted energy storage areas. The more corresponding areas there are, the more meaningful it is to set up an energy storage node at the intersection. Arrange the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection, then select the intersections one by one and install the energy storage node in the intersection.

[0140] It should be noted that the predicted energy storage area itself is a spatial range, and the intersection is actually also a range. Energy storage nodes are set within the intersection and a random setting method can be used as long as the energy storage nodes are ensured to be within the intersection.

[0141] Example 6:

[0142] During the installation of energy storage nodes, each time an energy storage node is installed, the predicted energy storage area corresponding to the energy storage node is eliminated once in the process of calculating the intersection of all predicted energy storage areas.

[0143] The above content adds a simplified solution. Every time a storage node is set up, the corresponding predicted storage area is deleted, indicating that the corresponding output node already has auxiliary energy supply equipment. This can prevent the storage nodes from being too concentrated. That is, one output node can be powered by multiple storage nodes.

[0144] In fact, both solutions provided in this application are feasible. What is affected is the number of energy storage nodes to be set up and the cost issue.

[0145] Embodiment seven:

[0146] Figure 2The structure diagram of the microgrid configuration system for weak substations in mountainous areas is shown. In a preferred embodiment of the technical solution of the present invention, a microgrid configuration system for weak substations in mountainous areas is also provided. The system 10 includes:

[0147] The monitoring point determination module 11 is used to obtain the distribution transformer of the substation 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;

[0148] A two-dimensional processing module 12 is used to obtain line data based on the instrument installed at the output point and the instrument installed at the monitoring point, and perform two-dimensional processing on the line data;

[0149] The spatial analysis module 13 is used to identify the line data after two-dimensional processing and determine the spatial abnormality of each output point;

[0150] 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;

[0151] The energy storage node setting module 15 is used to count the spatial anomaly and temporal anomaly of all output points and determine the energy storage nodes.

[0152] Furthermore, the monitoring point determination module 11 includes:

[0153] A data query unit is used to obtain the distribution transformers installed in the substation area and query the power lines in the substation area using the distribution transformers as line nodes;

[0154] A detection density calculation unit is used to obtain output points on the power line and calculate the detection density at each position in the power line according to the output points;

[0155] A detection density update unit, used to select monitoring points based on the detection density and synchronously update the detection density;

[0156] The loop execution unit is used for loop execution 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.

[0157] Specifically, the two-dimensional processing module 12 includes:

[0158] A channel establishing unit is used to establish a connection channel with an instrument installed at an output point and a connection channel with an instrument installed at a monitoring point;

[0159] A channel application unit is used to obtain line data containing location tags based on the established connection channel;

[0160] A normalization processing unit is used to perform time domain registration on the line data and normalize the line data with location tags at the same time;

[0161] The data arrangement unit is used to arrange the normalized data based on the position labels.

[0162] In addition, the spatial analysis module 13 includes:

[0163] A two-dimensional data processing unit, used to perform a two-dimensional Fourier transform on the line data after two-dimensional processing, and extract a spectrum diagram and a phase diagram;

[0164] an inverse transformation unit, configured to perform high-pass filtering of different sizes on the spectrum graph, and perform inverse transformation on the spectrum graph after high-pass filtering based on the phase graph;

[0165] A position query unit, used to query the position of the retained data in the result obtained by the inverse transformation;

[0166] an abnormality calculation unit, for determining the spatial abnormality of the position of the retained data according to the size of the high-pass filter;

[0167] 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 anomaly degree is proportional to the maximum radius corresponding to each position.

[0168] Furthermore, the time analysis module 14 includes:

[0169] The data difference obtaining unit is used to count the line data of each output point and obtain the data difference of the line data;

[0170] A data difference query unit is used to query the data difference within a preset backtracking time starting from the current time;

[0171] A mean calculation unit is used to perform a de-extreme value process on the data differences within the retrospective time at a preset ratio, and calculate the standard deviation of the data differences after the de-extreme value process;

[0172] The execution unit is used to determine the temporal anomaly degree of the output point according to the direct proportion of the standard deviation.

[0173] In addition, the energy storage node setting module 15 includes:

[0174] The data statistics unit is used to count the spatial anomaly and temporal anomaly of all output points and calculate the comprehensive anomaly;

[0175] a prediction unit, configured to determine a predicted energy storage area based on the comprehensive abnormality; the predicted energy storage area being a circular area whose radius is inversely proportional to the comprehensive abnormality, and the predicted energy storage area being used to characterize the installation range of the energy storage node supplying power to the output point;

[0176] A descending order arrangement unit is used to calculate the intersection of all predicted energy storage areas and arrange the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection;

[0177] 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 number threshold.

[0178] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A microgrid configuration method for weak substations in mountainous areas, characterized in that: The method comprises: Obtain the distribution transformer of the substation 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; 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; Identify the line data after two-dimensional processing and determine the spatial abnormality of each output point; Identify the line data of each output point and determine the time anomaly of each output point; Count the spatial and temporal anomalies of all output points to determine the energy storage nodes; The step of identifying the two-dimensionally processed line data and determining the spatial abnormality of each output point includes: Perform two-dimensional Fourier transform on the two-dimensional line data to extract the spectrum and phase diagram; Perform high-pass filtering of different sizes on the spectrum graph, and perform inverse transformation on the high-pass filtered spectrum graph based on the phase image; In the result obtained by the inverse transformation, query the location of the retained data; Determine the spatial abnormality of the location of the retained data according to 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 adopts the radius of the circle; the spatial anomaly degree is proportional to the maximum radius corresponding to each position; The step of identifying the line data of each output point and determining the time anomaly of each output point includes: Count the line data of each output point and calculate the data difference of the line data; Starting from the current moment, query the data difference within the preset backtracking time; Perform a preset ratio of de-extreme value processing on the data differences within the retrospective time, and calculate the standard deviation of the data differences after de-extreme value processing; Determining the temporal anomaly of the output point according to the direct proportion of the standard deviation; The step of counting the spatial anomaly and temporal anomaly of all output points to determine the energy storage node includes: Count the spatial anomaly and temporal anomaly of all output points and calculate the comprehensive anomaly; Determine a predicted energy storage area based on the comprehensive abnormality; the predicted energy storage area is a circular area, the radius of which is inversely proportional to the comprehensive abnormality, and the predicted energy storage area is used to represent 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 according to the number of predicted energy storage areas corresponding to each intersection; Energy storage nodes are installed in the intersection in sequence until the number of energy storage nodes reaches a preset threshold.

2. The microgrid configuration method for weak substations in mountainous areas according to claim 1 is characterized in that: The steps of obtaining the distribution transformer of the substation and the power line based on the distribution transformer, obtaining the output point of the power line, and determining the monitoring point in the power line according to the output point include: Obtain the distribution transformers installed in the substation area, and use the distribution transformers as line nodes to query the power lines within the substation area; Obtain output points on the power line, and calculate the detection density at each position in the power line based on the output points; Select monitoring points based on detection density and update detection density simultaneously; The loop is executed 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.

3. The microgrid configuration method for weak substations in mountainous areas according to claim 2 is characterized in that: The step of obtaining line data based on the instrument installed at the output point and the instrument installed at the monitoring point, and performing two-dimensional processing on the line data includes: Establish a connection channel with the instrument installed at the output point, and establish a connection channel with the instrument installed at the monitoring point; Obtaining line data containing location tags based on the established connection channel; Perform time domain registration on the line data and normalize the line data with location tags at the same time; Arrange the normalized data based on position labels.

4. The microgrid configuration method for weak substations in mountainous areas according to claim 1 is characterized in that: During the installation of energy storage nodes, each time an energy storage node is installed, the predicted energy storage area corresponding to the energy storage node is eliminated once in the process of calculating the intersection of all predicted energy storage areas.

5. A microgrid configuration system for weak substations in mountainous areas, characterized by: The system comprises: A monitoring point determination module is used to obtain the distribution transformer of the substation 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; A two-dimensional processing module is used to 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; The spatial analysis module is used to identify the line data after two-dimensional processing and determine the spatial abnormality of each output point; The time analysis module is used to identify the line data of each output point and determine the time anomaly of each output point; The energy storage node setting module is used to count the spatial and temporal anomalies of all output points and determine the energy storage nodes; The spatial analysis module includes: A two-dimensional data processing unit, used to perform a two-dimensional Fourier transform on the line data after two-dimensional processing, and extract a spectrum diagram and a phase diagram; an inverse transformation unit, configured to perform high-pass filtering of different sizes on the spectrum graph, and perform inverse transformation on the spectrum graph after high-pass filtering based on the phase graph; A position query unit, used to query the position of the retained data in the result obtained by the inverse transformation; an abnormality calculation unit, for determining the spatial abnormality of the position of the retained data according to 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 adopts the radius of the circle; the spatial anomaly degree is proportional to the maximum radius corresponding to each position; The time analysis module includes: The data difference obtaining unit is used to count the line data of each output point and obtain the data difference of the line data; A data difference query unit is used to query the data difference within a preset backtracking time starting from the current time; A mean calculation unit is used to perform a de-extreme value process on the data differences within the retrospective time at a preset ratio, and calculate the standard deviation of the data differences after the de-extreme value process; an execution unit, configured to determine a temporal anomaly degree of an output point according to a direct ratio of the standard deviation; The energy storage node setting module includes: The data statistics unit is used to count the spatial anomaly and temporal anomaly of all output points and calculate the comprehensive anomaly; a prediction unit, configured to determine a predicted energy storage area based on the comprehensive abnormality; the predicted energy storage area being a circular area whose radius is inversely proportional to the comprehensive abnormality, and the predicted energy storage area being used to characterize the installation range of the energy storage node supplying power to the output point; A descending order arrangement unit is used to calculate the intersection of all predicted energy storage areas and arrange the intersections in descending order according to the number of predicted energy storage areas corresponding to each intersection; 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 number threshold.

6. The microgrid configuration system for weak substations in mountainous areas according to claim 5 is characterized in that: The monitoring point determination module includes: A data query unit is used to obtain the distribution transformers installed in the substation area and query the power lines in the substation area using the distribution transformers as line nodes; A detection density calculation unit is used to obtain output points on the power line and calculate the detection density at each position in the power line according to the output points; A detection density update unit, used to select monitoring points based on the detection density and synchronously update the detection density; The loop execution unit is used for loop execution 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.

7. The microgrid configuration system for weak substations in mountainous areas according to claim 5 is characterized in that: The two-dimensional processing module includes: A channel establishing unit is used to establish a connection channel with an instrument installed at an output point and a connection channel with an instrument installed at a monitoring point; A channel application unit is used to obtain line data containing location tags based on the established connection channel; A normalization processing unit is used to perform time domain registration on the line data and normalize the line data with location tags at the same time; The data arrangement unit is used to arrange the normalized data based on the position labels.

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