Low-voltage line branch detection method and device, computer equipment and storage medium

Abstract

The present application relates to a low-voltage line branch detection method and device, computer equipment and storage medium. The method includes: obtaining abnormal branch current according to the residual current array of the main node, and obtaining the first correlation coefficient of the voltage and current corresponding to the branch node, and forming a first set of branch nodes corresponding to the first correlation coefficient whose absolute value is greater than a fixed value; obtaining a third set of branch nodes whose voltage increment and current increment do not match the main node, and a fourth set of branch nodes whose voltage increment does not match the current increment of the main node, and obtaining a passive node set and an active node set with abnormal branch nodes according to the above three sets; constructing a branch line topology structure and calculating the point distance between the abnormal branch node and the corresponding passive node according to the passive node set and the active node set, so as to locate the abnormal branch according to the branch line topology structure, point distance and abnormal branch current. The use of this method can locate the abnormal branch more timely and accurately.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a low-voltage line branch detection method and device, computer equipment, storage medium, and computer program product. Background Art

[0002] With the development of power system technology, the structure of low-voltage power grids has become increasingly complex. Because low-voltage power grids directly serve consumers, the phenomenon of unauthorized wiring and connection of low-voltage lines is serious and difficult to detect. This unauthorized wiring and connection of low-voltage lines causes significant economic losses and poses significant safety risks. Existing technologies have limited timeliness and accuracy in locating abnormal branches in low-voltage lines. Summary of the Invention

[0003] Based on this, it is necessary to provide a low-voltage line branch detection method and device, computer equipment, storage medium and computer program product that can locate abnormal branches in low-voltage lines more timely and accurately to address the above technical problems.

[0004] In a first aspect, the present application provides a low-voltage line branch detection method, wherein the low-voltage line includes multiple main nodes, each main node corresponds to multiple branch nodes; the low-voltage line branch detection method includes:

[0005] When it is determined that an abnormal branch exists in the low-voltage line, the abnormal branch current is obtained based on the residual current array of the main node, and when the abnormal branch current is greater than a preset first threshold, a first correlation coefficient between the voltage and current corresponding to each branch node is obtained, and each branch node corresponding to the first correlation coefficient having an absolute value greater than a preset value is formed into a first set;

[0006] Obtaining a third set of branch nodes whose voltage increments and current increments do not match those of the main node, and a fourth set of branch nodes whose voltage increments do not match those of the main node, and obtaining a passive node set and an active node set based on the third set, the fourth set, and the first set, wherein there is at least one abnormal branch node in the active node set;

[0007] Obtaining a correlation coefficient matrix between the abnormal branch node and the corresponding passive node according to the passive node set and the active node set, constructing a branch line topology structure and calculating the point distance between the abnormal branch node and the corresponding passive node according to the correlation coefficient matrix, so as to determine the position of the abnormal branch according to the branch line topology structure, the point distance and the abnormal branch current;

[0008] Among them, the first set refers to the set of branch nodes in which the voltage increment and current increment of this node are associated, the passive node set refers to the set of branch nodes in which the voltage increment and voltage increment change passively occur, and the active node set refers to the set of branch nodes in which the voltage increment and voltage increment change actively occur.

[0009] In one embodiment, the low-voltage line branch detection method further includes:

[0010] Integrating the residual current array of the master node with respect to time to generate a residual current mean value of the master node;

[0011] When the residual current mean value is greater than a preset second threshold, it is determined that an abnormal branch exists in the low-voltage line.

[0012] In one embodiment, obtaining a third set of branch nodes whose voltage increments and current increments do not match those of the main node and a fourth set of branch nodes whose voltage increments do not match those of the main node includes:

[0013] Get the maximum current array and voltage array of each branch node and the initial current array of the main node;

[0014] respectively obtaining a second correlation coefficient between the maximum value of the current array and the initial current array and a third correlation coefficient between the maximum value of the voltage array and the initial current array;

[0015] The branch nodes corresponding to the second correlation coefficients having values ​​greater than 0.55 and not greater than 0.55 are respectively formed into a second set and a third set, and the branch nodes corresponding to the third correlation coefficients having values ​​not greater than 0.35 are formed into a fourth set, where the second set refers to the set of branch nodes whose voltage increments and current increments match those of the main node;

[0016] Obtaining a current array corresponding to each branch node in the second set, and subtracting each current array from the initial current array to update the initial current array;

[0017] The second correlation coefficient, the third correlation coefficient, the second set, the third set, and the fourth set are repeatedly obtained based on the maximum current array value and the maximum voltage array value of each branch node and the updated initial current array of the main node, and the initial current array is repeatedly updated until the mean value of the initial current array is less than 1A. The remaining current array of the main node refers to the initial current array with a mean value less than 1A obtained after the last update.

[0018] In one embodiment, obtaining the maximum current array value, the maximum voltage array value of each branch node, and the initial current array of the main node includes:

[0019] The current and voltage of each branch node in the current period are respectively collected to generate a current array and a voltage array of each branch node, and the maximum current array value and the maximum voltage array value of each branch node are obtained according to the current array and the voltage array;

[0020] The current of the master node in the previous period is collected to obtain an initial current array of the master node.

[0021] In one embodiment, obtaining the passive node set and the active node set according to the third set, the fourth set, and the first set includes:

[0022] The branch nodes corresponding to the third correlation coefficient having a value greater than 0.35 are grouped into a fifth set, and the first set is updated based on the second set and the fifth set. The fifth set is a set of branch nodes whose voltage increments match the current increments of the main node.

[0023] The passive node set and the active node set are obtained according to the third set, the fourth set and the updated first set.

[0024] In one embodiment, obtaining the passive node set and the active node set according to the third set, the fourth set, and the updated first set includes:

[0025] Dividing the updated first set into a passive node set and an active node set, wherein the first correlation coefficients corresponding to each branch node in the passive node set and the active node set are positive and negative respectively;

[0026] A union logic operation is performed on the third set and the fourth set to obtain a sixth set, and the branch nodes in the sixth set are subtracted from the passive node set and the active node set to update the passive node set and the active node set.

[0027] In a second aspect, the present application further provides a low-voltage line branch detection device, wherein the low-voltage line includes multiple main nodes, each main node corresponds to multiple branch nodes; the low-voltage line branch detection device includes:

[0028] an abnormality determination module, configured to, upon determining that an abnormal branch exists in the low-voltage line, obtain the abnormal branch current based on the residual current array of the main node, and when the abnormal branch current is greater than a preset first threshold, obtain a first correlation coefficient between the voltage and current corresponding to each branch node, and group the branch nodes corresponding to the first correlation coefficient whose absolute value is greater than the preset value into a first set;

[0029] a set acquisition module, configured to acquire a third set of branch nodes whose voltage increments and current increments do not match those of the main node, and a fourth set of branch nodes whose voltage increments do not match those of the main node; and acquire a passive node set and an active node set based on the third set, the fourth set, and the first set, wherein the active node set contains at least one abnormal branch node;

[0030] An abnormality positioning module is used to obtain a correlation coefficient matrix between abnormal branch nodes and corresponding passive nodes based on the passive node set and the active node set, construct a branch line topology structure and calculate the point distance between the abnormal branch node and the corresponding passive node based on the correlation coefficient matrix, so as to determine the position of the abnormal branch based on the branch line topology structure, point distance and abnormal branch current;

[0031] Among them, the first set refers to the set of branch nodes in which the voltage increment and current increment of this node are associated, the passive node set refers to the set of branch nodes in which the voltage increment and voltage increment change passively occur, and the active node set refers to the set of branch nodes in which the voltage increment and voltage increment change actively occur.

[0032] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0033] In a fourth aspect, the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0034] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0035] The above-mentioned low-voltage line branch detection method and apparatus, computer device, storage medium, and computer program product, upon determining the presence of an abnormal branch in a low-voltage line, obtain the abnormal branch current based on the residual current array of the main node. When the abnormal branch current exceeds a preset first threshold, it indicates that the abnormal branch will cause a significant safety hazard and needs to be located and identified to eliminate the safety hazard. Furthermore, a first correlation coefficient of the voltage and current corresponding to each branch node is obtained in real time, and the branch nodes corresponding to the first correlation coefficient whose absolute value is greater than a preset value are formed into a first set. Furthermore, a third set of branch nodes whose voltage increments and current increments do not match those of the main node and a fourth set of branch nodes whose voltage increments do not match those of the main node are obtained. Based on the third set, the fourth set, and the first set, a passive node set and an active node set are obtained, wherein at least one abnormal branch node exists in the active node set. Furthermore, a correlation coefficient matrix between the abnormal branch node and the corresponding passive node is obtained based on the passive node set and the active node set. Based on the correlation coefficient matrix, a branch line topology is constructed and the point distance between the abnormal branch node and the corresponding passive node is calculated. Ultimately, the location of the abnormal branch is determined based on the branch line topology, the point distance, and the abnormal branch current. Among them, the first set refers to the set of branch nodes associated with the voltage increment and current increment of this node, the passive node set refers to the set of branch nodes that passively generate voltage increment and voltage increment change, and the active node set refers to the set of branch nodes that actively generate voltage increment and voltage increment change. The above method is used to detect the low-voltage line branch in real time, and when it is determined that there is an abnormal branch, the voltage, current and related parameters of the main node and each branch node and the relationship between each parameter are obtained or calculated in real time. Then, the abnormal branch is located more timely and accurately through the branch line topology, the point distance between the abnormal branch node and the corresponding passive node, and the abnormal branch current, and then the load overload warning information is reliably provided to eliminate the safety hazards existing in the low-voltage line more timely and accurately. At the same time, it can also provide effective data support for low-voltage line line loss analysis and electricity metering. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A diagram showing an application environment of a low-voltage line branch detection method in one embodiment;

[0037] Figure 2 This is a flow chart of a low-voltage line branch line detection method according to one embodiment;

[0038] Figure 3 This is a second flow chart of a low-voltage line branch line detection method according to an embodiment;

[0039] Figure 4A schematic diagram of a process for obtaining a third set of branch nodes whose voltage increments and current increments do not match those of a main node and a fourth set of branch nodes whose voltage increments do not match those of a main node in one embodiment;

[0040] Figure 5 A schematic diagram of a process for obtaining the maximum current array value, the maximum voltage array value, and the initial current array value of each branch node and the main node in one embodiment;

[0041] Figure 6 A schematic diagram of a process for obtaining a passive node set and an active node set according to a third set, a fourth set, and a first set in one embodiment;

[0042] Figure 7 A schematic diagram of a process for obtaining a passive node set and an active node set according to a third set, a fourth set, and an updated first set in one embodiment;

[0043] Figure 8 1 is a structural block diagram of a low-voltage line branch detection device in one embodiment;

[0044] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment.

[0045] Description of Figure Numbers:

[0046] Low-voltage line branch detection device: 10; abnormality determination module: 11; set acquisition module: 12; abnormality positioning module: 13. DETAILED DESCRIPTION

[0047] To facilitate understanding of the embodiments of the present application, a more comprehensive description of the embodiments of the present application will be provided below with reference to the accompanying drawings. The accompanying drawings provide preferred embodiments of the embodiments of the present application. However, the embodiments of the present application can be implemented in many different forms and are not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive disclosure of the embodiments of the present application.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of the present application. The terms used herein in the description of the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application.

[0049] It is understood that the term "include / comprise" specifies the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but does not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. The terms "first," "second," and the like may be used herein to describe various parameters, but these parameters are not limited by these terms. These terms are only used to distinguish a first parameter from another parameter. For example, without departing from the scope of this application, a first set may be referred to as a second set, and a second set may be referred to as a first set; a first correlation coefficient may be referred to as a second correlation coefficient, and a second correlation coefficient may be referred to as a first correlation coefficient.

[0050] Example 1

[0051] Recently, smart meters and smart distribution terminals have been widely used, and the reliability of electricity consumption information data collection has been improved. The mining and use of these data has become an application trend in the industry. However, the calculation of the big data background is affected by its own delay characteristics and is only suitable for scenarios with low delay requirements such as line topology and line loss analysis. With the widespread application of AI technology and the promotion of edge computing technology, the metering and collection equipment at the end of the power grid has become more and more intelligent based on intelligent and effective algorithms. Using the high-performance embedded processors of these smart devices to realize local calculations and obtain the characteristic information of the low-voltage distribution network will become a trend in the construction of smart grid infrastructure. The embodiment of the present application provides a low-voltage line branch detection method, which is applicable based on the above-mentioned embedded processor.

[0052] like Figure 1 The figure shows the application environment of the low-voltage line branch detection method. There are many branches of the low-voltage line, and its application environment is generally composed of a main control switch cabinet, a branch box, a meter box, an electric meter, and user equipment. Among them, the main node is generally the main control switch cabinet. The intelligent terminal here is responsible for monitoring the voltage and current of the entire substation. Its current is equal to the vector sum of the currents of each branch line, and the voltage is directly related to the voltage of each branch node. The branch node is generally a meter box terminal and an electric meter. The branch box divides a main line into n branch boxes connected to the meter box. n is generally 3, 4 or 5. The electric meter is installed in the meter box, and the electrical equipment is connected to the output end of the meter. When a certain user equipment is working stably, its working current passes through the substation transformer, main line, branch line, branch box, meter box, electric meter, user-side line and then to the electrical equipment. Due to the certain impedance of the line, this current will produce a voltage drop on the line. This voltage drop will be detected by the meter. At this time, the voltage change at the main node is small and the current change is Larger, and and The correlation coefficient will be affected by the low-pass effect of the low-voltage line itself and the superposition effect of the currents of multiple branch lines. It should be noted that the correlation coefficient, the first correlation coefficient, the second correlation coefficient and the third correlation coefficient mentioned in the embodiments of this application all refer to the Pearson correlation coefficient, which will not be repeated later.

[0053] The voltage and current at the branch nodes are collected and recorded every 1 minute to form an incremental curve of voltage and current. The voltage and current at the main node are collected and recorded every 1 second to form an incremental curve of voltage and current in the substation. Under normal circumstances, the working status of each electrical equipment is within the monitoring range, and the incremental curve of voltage and current of each branch node is in correspondence with the incremental curve of the main node. Among them, the incremental curve of voltage and current is the difference between the effective values ​​of two adjacent sampling points. The incremental curve will effectively eliminate the influence of the stable DC component of the low-voltage power grid on the calculation cycle. In actual applications, due to the abnormal behavior of some users, the main line has unexpected branches, resulting in the incremental curve of voltage and current of each branch node being inconsistent with the incremental curve of the main node. Therefore, the embodiment of the present application uses existing collection equipment, combined with the characteristics of HPLC high-speed communication, to classify the collected data according to the characteristics of the correlation coefficient, and on this basis, combines the characteristics of the low-voltage line itself to locate the abnormal branch.

[0054] like Figure 2 As shown, the low-voltage line includes multiple main nodes, each main node corresponds to multiple branch nodes, and the low-voltage line branch detection method includes steps 210 to 230.

[0055] In step 210, if an abnormal branch is determined to exist in the low-voltage line, the abnormal branch current is obtained based on the residual current array of the main node. When the abnormal branch current is greater than a preset first threshold, a first correlation coefficient between the voltage and current corresponding to each branch node is obtained. Branch nodes corresponding to the first correlation coefficient whose absolute value is greater than the preset value are grouped into a first set. The first set is the set of branch nodes for which the voltage increment and current increment of the node are correlated.

[0056] In this embodiment, if an abnormal branch is determined to exist in a low-voltage line, the magnitude of the non-correlated abnormal branch current is obtained by subtracting the correlated current of each branch node from the current of the main node. The first threshold value can be set based on actual conditions. When the abnormal branch current is greater than the preset first threshold value, it indicates that the abnormal branch will cause a significant safety hazard and needs to be located and identified to eliminate the safety hazard. It is understood that when the abnormal branch current is less than the preset first threshold value, the likelihood of the abnormal branch causing a safety hazard is relatively small and can be ignored. In this case, there is no need to locate the abnormal branch, and the next cycle of low-voltage line branch detection can be directly entered.

[0057] In this embodiment, the preset value can be set to 0.8, that is, when the absolute value of the first correlation coefficient is greater than 0.8, the corresponding branch nodes are formed into a first set, wherein the association between the voltage increment and the current increment of this node means that the change of the voltage increment curve of this node is caused by the change of the current increment curve of this node, that is, caused by the load change of this node.

[0058] Preferably, if Figure 3 As shown, the low-voltage line branch detection method includes steps 310 to 350, wherein steps 330 to 350 are the same as Figure 2 Steps 210 to 230 correspond to each other, and the description can refer to Figure 2 Before executing step 330 , the low-voltage line branch detection method further includes steps 310 to 320 .

[0059] Step 310 integrates the residual current array of the master node with respect to time to generate a mean residual current value of the master node. In this embodiment, the residual current array of the master node refers to the residual current array formed by subtracting the current array of the branch node matched with the master node from the initial current array corresponding to the initial current increment curve collected by the master node.

[0060] Step 320: When the mean residual current exceeds a preset second threshold, an abnormal branch is determined to exist in the low-voltage line. In this embodiment, the second threshold is adjustable based on the scale of the substation and is a tunable parameter. When the mean residual current exceeds the second threshold, indicating a significant load or current change, an abnormal branch is determined to exist in the low-voltage line.

[0061] In this embodiment, based on the characteristic that load changes cause terminal voltage increment changes, a correlation analysis is performed at the master node to determine the presence of non-correlated current loads in the branch line, i.e., the presence of an abnormal branch. This is then followed by a location analysis of the abnormal branch to promptly and accurately identify its specific location.

[0062] Step 220: Obtain a third set of branch nodes whose voltage increments and current increments do not match those of the main node, and a fourth set of branch nodes whose voltage increments do not match those of the main node. A passive node set and an active node set are obtained based on the third set, the fourth set, and the first set, wherein at least one abnormal branch node exists in the active node set. The passive node set refers to the set of branch nodes whose voltage increments and voltage increments change passively, and the active node set refers to the set of branch nodes whose voltage increments and voltage increments change actively.

[0063] Preferably, if Figure 4As shown, obtaining a third set of branch nodes whose voltage increments and current increments do not match those of the main node and a fourth set of branch nodes whose voltage increments do not match those of the main node includes steps 410 to 450 .

[0064] Step 410 , obtaining the maximum current array value, the maximum voltage array value of each branch node and the initial current array value of the main node.

[0065] Preferably, if Figure 5 As shown, obtaining the maximum current array value, the maximum voltage array value of each branch node and the initial current array of the main node includes steps 510 to 520.

[0066] Step 510 collects the current and voltage of each branch node during the current time period to generate a current array and a voltage array for each branch node. The maximum current array and the maximum voltage array for each branch node are then obtained based on the current array and the voltage array. In this embodiment, each meter box terminal and meter collects the voltage and current increment curves during the current time period. The corresponding sequences of the voltage and current increment curves are then arranged in descending order to generate the current array and the voltage array for each branch node, where each element is arranged by time point.

[0067] Step 520: Collect the current at the master node during the previous period to obtain the master node's initial current array. Every minute, the intelligent terminal of the master switchgear correlates the meter data collected over the previous 60 minutes with the node itself, forming a voltage and current increment curve for the master node. This current increment curve then generates the master node's initial current array. It's important to note that to reduce computational complexity, when the master node detects a current increment in the low-voltage line that exceeds a threshold, it determines whether an abnormal branch exists. This threshold is set based on actual conditions and can be set to 2A.

[0068] Step 420 , respectively obtain a second correlation coefficient between the maximum value of the current array and the initial current array and a third correlation coefficient between the maximum value of the voltage array and the initial current array.

[0069] In step 430, the branch nodes corresponding to the second correlation coefficients greater than 0.55 and less than 0.55 are grouped into a second set and a third set, respectively. The branch nodes corresponding to the third correlation coefficients less than 0.35 are grouped into a fourth set. The second set is the set of branch nodes whose voltage and current increments match those of the main node. In this embodiment, the voltage and current increments caused by load changes at the branch line terminals of the branch nodes in the second set match those of the main node, indicating that these branch lines are normal and the corresponding electrical equipment belongs to the substation. Conversely, the electrical equipment corresponding to the branch nodes in the third set may not belong to the substation.

[0070] Step 440: Obtain the current array corresponding to each branch node in the second set and subtract each current array from the initial current array to update the initial current array. In this embodiment, the initial current array refers to the initial current array corresponding to the initial current increment curve collected by the master node. The currents of the branch nodes in the second set are subtracted from the current of the master node, and the subtraction is performed exponentially at boundary points to further update the initial current array. Updating the initial current array can obtain the residual current array of the master node to obtain the abnormal branch current and determine whether the abnormal branch needs to be located.

[0071] In step 450, the second correlation coefficient, third correlation coefficient, second set, third set, and fourth set are repeatedly obtained based on the maximum current array and voltage array values ​​of each branch node and the updated initial current array of the main node, and the initial current array is repeatedly updated until the mean value of the initial current array is less than 1 A. The remaining current array of the main node refers to the last updated and obtained initial current array with a mean value less than 1 A. It will be understood that in this embodiment, the third and fourth sets obtained last and their respective branch nodes are used as the basis. The fourth set corresponds to the branch nodes of the third set, indicating that the included branch nodes do not belong to the current station area.

[0072] Preferably, if Figure 6 As shown, obtaining the passive node set and the active node set according to the third set, the fourth set and the first set includes steps 610 to 620.

[0073] In step 610, each branch node corresponding to the third correlation coefficient having a value greater than 0.35 is grouped into a fifth set, and the first set is updated based on the second and fifth sets. The fifth set is the set of branch nodes whose voltage increments match the current increments of the master node. In this embodiment, the fifth set corresponds to the branch nodes in the second set. Since the branch nodes in the second set have both voltage increments and current increments that match those of the master node, the first set is updated based on the second and fifth sets. The fifth set can be used as a supplement to the second set to avoid including branch nodes that do not belong to the current substation in the first set, while also avoiding errors in the third set.

[0074] Step 620: Acquire a passive node set and an active node set according to the third set, the fourth set, and the updated first set.

[0075] Preferably, if Figure 7 As shown, obtaining the passive node set and the active node set according to the third set, the fourth set and the updated first set includes steps 710 to 720.

[0076] Step 710 divides the updated first set into a passive node set and an active node set. The first correlation coefficients corresponding to the branch nodes in the passive node set and the active node set are positive and negative, respectively. In this embodiment, a positive first correlation coefficient indicates that the voltage and current increments are passively caused by external factors, while a negative first correlation coefficient indicates that the voltage and current increments are actively caused by changes in the load itself.

[0077] Step 720: Perform a union logic operation on the third set and the fourth set to obtain a sixth set, and subtract the branch nodes in the sixth set from the passive node set and the active node set to update the passive node set and the active node set. In this embodiment, since the third set and the fourth set have a corresponding relationship, both represent that the included branch nodes do not belong to this station area. Therefore, performing a union logic operation on these two sets can include all branch nodes that do not belong to this station area. In this embodiment, when the passive node set is an empty set and the active node set is a full set, it indicates that an abnormal branch exists at this time.

[0078] Step 230: Obtain a correlation coefficient matrix between the abnormal branch node and the corresponding passive node based on the passive node set and the active node set, construct a branch line topology structure based on the correlation coefficient matrix, and calculate the point distance between the abnormal branch node and the corresponding passive node, so as to determine the position of the abnormal branch based on the branch line topology structure, the point distance, and the abnormal branch current.

[0079] In this embodiment, active and passive nodes are physically connected. When a branch line is loaded at its end, the line impedance causes a voltage change at a location upstream of the branch line, resulting in a passive node at that location. This relationship between the branch nodes is known. It is understood that the degree of impact and the degree of association vary depending on the distance of the upstream location from the loaded branch line. Based on the degree of association between the passive node set and the branch nodes in the active node set, i.e., the correlation coefficient matrix between the two, the affiliation between the abnormal branch node and the corresponding passive node is determined, and the branch line topology of the substation can be constructed. Simultaneously, through statistical methods, the distance from the abnormal branch node to each corresponding passive node can be further determined based on the correlation coefficient matrix. In this embodiment, based on the characteristics of the line impedance distribution, the intelligent terminal at the main node first calculates the impedance value from the main node to the branch line end based on the current increment. Then, based on the voltage increment at the branch line end and the current increment at the main node, the impedance at the abnormal branch node is estimated, and the magnitude of the abnormal branch current is determined. Furthermore, the abnormal branch can be located based on the branch line topology, point spacing, and abnormal branch current.

[0080] The low-voltage line branch detection method in the embodiment of the present application is used to perform real-time detection of the low-voltage line branch. When it is determined that there is an abnormal branch, the voltage, current and related parameters of the main node and each branch node and the relationship between each parameter are obtained or calculated in real time. Then, the abnormal branch is located more timely and accurately through the branch line topology, the point distance between the abnormal branch node and the corresponding passive node, and the abnormal branch current, and then load overload warning information is reliably provided, so as to eliminate the safety hazards existing in the low-voltage line more timely and accurately, and at the same time, it can also provide effective data support for low-voltage line line loss analysis and electricity metering.

[0081] It should be understood that although the flowcharts involved in the above embodiments Figure 2-Figure 7 The steps in the flowchart are shown in the order indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Figure 2-Figure 7 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0082] Example 2

[0083] Based on the same inventive concept, embodiments of the present application also provide a low-voltage line branch detection device for implementing the low-voltage line branch detection method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more embodiments of the low-voltage line branch detection device provided below can be found in the above-mentioned limitations of the low-voltage line branch detection method and will not be further elaborated here.

[0084] like Figure 8As shown, an embodiment of the present application also provides a low-voltage line branch detection device. The low-voltage line includes multiple main nodes, each main node corresponds to multiple branch nodes, and the low-voltage line branch detection device 10 includes an abnormality determination module 11, a set acquisition module 12, and an abnormality positioning module 13. The abnormality determination module 11 is used to obtain the abnormal branch current based on the residual current array of the main node when it is determined that there is an abnormal branch in the low-voltage line. When the abnormal branch current is greater than a preset first threshold, the first correlation coefficient of the voltage and current corresponding to each branch node is obtained, and the branch nodes corresponding to the first correlation coefficient whose absolute value is greater than the preset value are formed into a first set. The set acquisition module 12 is used to obtain a third set of branch nodes whose voltage increment and current increment do not match the main node, and a fourth set of branch nodes whose voltage increment does not match the current increment of the main node. Based on the third set, the fourth set, and the first set, a passive node set and an active node set are obtained, and at least one abnormal branch node exists in the active node set. The abnormality location module 13 is configured to obtain a correlation coefficient matrix between abnormal branch nodes and corresponding passive nodes based on the passive node set and the active node set, construct a branch line topology structure based on the correlation coefficient matrix, and calculate the point distance between the abnormal branch node and the corresponding passive node, thereby determining the location of the abnormal branch based on the branch line topology structure, the point distance, and the abnormal branch current. The first set refers to the set of branch nodes whose voltage increment and current increment are correlated, the passive node set refers to the set of branch nodes whose voltage increment and voltage increment change passively occur, and the active node set refers to the set of branch nodes whose voltage increment and voltage increment change actively occur.

[0085] Preferably, the low-voltage line branch detection device 10 further includes a current integration module and an abnormality determination module. The current integration module is configured to integrate the residual current array of the master node with respect to time to generate a residual current mean value of the master node. The abnormality determination module is configured to determine the presence of an abnormal branch in the low-voltage line when the residual current mean value is greater than a preset second threshold.

[0086] Preferably, the set acquisition module 12 includes a first parameter acquisition unit, a correlation coefficient acquisition unit, a second set generation unit, a second parameter acquisition unit, and an update acquisition unit. The first parameter acquisition unit is configured to acquire the maximum current array value, the maximum voltage array value, and the initial current array value of each branch node. The correlation coefficient acquisition unit is configured to respectively acquire the second correlation coefficient between the maximum current array value and the initial current array value, and the third correlation coefficient between the maximum voltage array value and the initial current array value. The second set generation unit is configured to respectively group the branch nodes corresponding to the second correlation coefficient values ​​greater than 0.55 and less than 0.55 into a second set and a third set, and to group the branch nodes corresponding to the third correlation coefficient values ​​less than 0.35 into a fourth set. The second set refers to the set of branch nodes whose voltage increments and current increments match those of the main node. The second parameter acquisition unit is configured to acquire the current arrays corresponding to the branch nodes in the second set and subtract each current array from the initial current array to update the initial current array. An updating and acquiring unit is configured to repeatedly acquire a second correlation coefficient, a third correlation coefficient, a second set, a third set, and a fourth set based on the maximum value of the current array, the maximum value of the voltage array, and the updated initial current array of the main node of each branch node, and repeatedly update the initial current array until the mean value of the initial current array is less than 1A. The remaining current array of the main node refers to the initial current array with a mean value less than 1A obtained after the last update.

[0087] Preferably, the above-mentioned first parameter acquisition unit is also used to respectively collect the current and voltage of each branch node in the current time period to generate the current array and voltage array of each branch node respectively, and obtain the maximum value of the current array and the maximum value of the voltage array of each branch node based on the current array and the voltage array; collect the current of the main node in the previous time period to obtain the initial current array of the main node.

[0088] Preferably, the above-mentioned set acquisition module 12 further includes a third set generation unit and a fifth set generation unit.

[0089] The third set generation unit is configured to group the branch nodes corresponding to the third correlation coefficient having a value greater than 0.35 into a fifth set, and to update the first set based on the second set and the fifth set, wherein the fifth set is a set of branch nodes whose voltage increments match the current increments of the master node. The fifth set generation unit is configured to obtain a passive node set and an active node set based on the third set, the fourth set, and the updated first set.

[0090] Preferably, the above-mentioned fifth set generation unit is also used to divide the updated first set into the passive node set and the active node set, and the first correlation coefficients corresponding to each branch node in the passive node set and the active node set are positive and negative values ​​respectively; perform a union logical operation on the third set and the fourth set to obtain a sixth set, and subtract the branch nodes in the sixth set from the passive node set and the active node set respectively to update the passive node set and the active node set.

[0091] Each module in the low-voltage line branch detection device 10 can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0092] Example 3

[0093] like Figure 9 As shown, an embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned low-voltage line branch detection method when executing the computer program.

[0094] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0095] Example 4

[0096] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned low-voltage line branch detection method when the computer program is executed by a processor.

[0097] Example 5

[0098] An embodiment of the present application further provides a computer program product, including a computer program, which implements the steps of the above-mentioned low-voltage line branch detection method when executed by a processor.

[0099] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0100] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A low-voltage line branch detection method, characterized in that: The low-voltage line includes a plurality of main nodes, each of the main nodes corresponds to a plurality of branch nodes; The method comprises: When it is determined that an abnormal branch exists in the low-voltage line, the abnormal branch current is obtained according to the residual current array of the main node, and when the abnormal branch current is greater than a preset first threshold, a first correlation coefficient between the voltage and the current corresponding to each of the branch nodes is obtained, and the branch nodes corresponding to the first correlation coefficients whose absolute values ​​are greater than a preset value are grouped into a first set; Obtaining a third set of branch nodes whose voltage increments and current increments do not match those of the main node, and a fourth set of branch nodes whose voltage increments do not match those of the main node, and obtaining a passive node set and an active node set based on the third set, the fourth set, and the first set, wherein there is at least one abnormal branch node in the active node set; Obtaining a correlation coefficient matrix between the abnormal branch node and the corresponding passive node according to the passive node set and the active node set, constructing a branch line topology structure according to the correlation coefficient matrix and calculating the point distance between the abnormal branch node and the corresponding passive node, so as to determine the position of the abnormal branch according to the branch line topology structure, the point distance and the abnormal branch current; Among them, the first set refers to the set of branch nodes in which the voltage increment and current increment of this node are associated, the passive node set refers to the set of branch nodes in which voltage increment and voltage increment change passively occur, and the active node set refers to the set of branch nodes in which voltage increment and voltage increment change actively occur.

2. The method according to claim 1, characterized in that The method further comprises: integrating the residual current array of the master node with respect to time to generate a residual current mean value of the master node; When the residual current mean value is greater than a preset second threshold, it is determined that the abnormal branch exists in the low-voltage line.

3. The method according to claim 1, characterized in that The acquiring of the third set of branch nodes whose voltage increments and current increments do not match those of the main node and the fourth set of branch nodes whose voltage increments do not match those of the main node includes: Obtaining the maximum current array value, the maximum voltage array value of each branch node and the initial current array of the main node; respectively obtaining a second correlation coefficient between the maximum value of the current array and the initial current array and a third correlation coefficient between the maximum value of the voltage array and the initial current array; The branch nodes corresponding to the second correlation coefficient having a value greater than 0.55 and not greater than 0.55 are respectively formed into a second set and a third set, and the branch nodes corresponding to the third correlation coefficient having a value not greater than 0.35 are formed into a fourth set, wherein the second set refers to the set of branch nodes whose voltage increment and current increment match the main node; Obtaining a current array corresponding to each branch node in the second set, and subtracting each current array from the initial current array to update the initial current array; Repeatedly obtain the second correlation coefficient, the third correlation coefficient, the second set, the third set, and the fourth set based on the maximum value of the current array of each branch node, the maximum value of the voltage array, and the updated initial current array of the main node, and repeatedly update the initial current array until the mean value of the initial current array is less than 1A. The remaining current array of the main node refers to the initial current array with a mean value less than 1A obtained during the last update.

4. The method according to claim 3, characterized in that The obtaining of the maximum current array value, the maximum voltage array value of each branch node and the initial current array of the main node includes: respectively collecting the current and voltage of each branch node in a current time period to generate the current array and voltage array of each branch node, and obtaining the maximum value of the current array and the maximum value of the voltage array of each branch node according to the current array and the voltage array; The current of the main node in a previous period is collected to obtain the initial current array of the main node.

5. The method according to claim 3, characterized in that The acquiring of the passive node set and the active node set according to the third set, the fourth set, and the first set includes: The branch nodes corresponding to the third correlation coefficient having a value greater than 0.35 are grouped into a fifth set, and the first set is updated according to the second set and the fifth set, where the fifth set refers to the set of branch nodes whose voltage increment matches the current increment of the main node; A passive node set and an active node set are obtained according to the third set, the fourth set and the updated first set.

6. The method according to claim 5, characterized in that The acquiring of the passive node set and the active node set according to the third set, the fourth set, and the updated first set includes: Dividing the updated first set into the passive node set and the active node set, wherein the first correlation coefficients corresponding to the branch nodes in the passive node set and the active node set are positive and negative respectively; A union logic operation is performed on the third set and the fourth set to obtain a sixth set, and the branch nodes in the sixth set are respectively subtracted from the passive node set and the active node set to update the passive node set and the active node set.

7. A low-voltage line branch detection device, characterized in that: The low-voltage line includes a plurality of main nodes, each of the main nodes corresponds to a plurality of branch nodes; The device comprises: an abnormality determination module, configured to, upon determining that an abnormal branch exists in the low-voltage line, obtain an abnormal branch current based on the residual current array of the main node, and when the abnormal branch current is greater than a preset first threshold, obtain a first correlation coefficient between the voltage and current corresponding to each of the branch nodes, and group the branch nodes corresponding to the first correlation coefficients having an absolute value greater than a preset value into a first set; a set acquisition module, configured to acquire a third set of branch nodes whose voltage increments and current increments do not match those of the main node, and a fourth set of branch nodes whose voltage increments do not match those of the main node, and acquire a passive node set and an active node set based on the third set, the fourth set, and the first set, wherein at least one abnormal branch node exists in the active node set; an abnormality locating module, configured to obtain a correlation coefficient matrix between the abnormal branch node and the corresponding passive node based on the passive node set and the active node set, construct a branch line topology structure based on the correlation coefficient matrix, and calculate a point distance between the abnormal branch node and the corresponding passive node, so as to determine the position of the abnormal branch based on the branch line topology structure, the point distance, and the abnormal branch current; Among them, the first set refers to the set of branch nodes in which the voltage increment and current increment of this node are associated, the passive node set refers to the set of branch nodes in which voltage increment and voltage increment change passively occur, and the active node set refers to the set of branch nodes in which voltage increment and voltage increment change actively occur.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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