A method and system for constructing a conduction map based on low-voltage area big data

Abstract

The present invention discloses a method and system for constructing a conduction map based on low-voltage substation big data, which collects the transient voltage of the meter box power load of the low-voltage substation meter box; collects the transient voltage of the transformer power load of the low-voltage substation transformer; calculates the correlation of the power load characteristic data between meter boxes and the power load characteristic data between transformers and outgoing line cabinets; determines the relationship between meter boxes and outgoing line cabinets and the relationship between transformers and outgoing line cabinets; forms a complete map of the low-voltage substation box transformer based on the relationship between meter boxes and outgoing line cabinets and the relationship between transformers and outgoing line cabinets. Advantages: Based on natural data of power load, it avoids the hidden dangers of power injection and reduces the workload of manual operation implementation; the power load characteristic data is valid data, which reduces the interference of native data signals and improves the information density. It captures data correlation information through first-order data, increases the accuracy of analysis results, and provides conditions for universal promotion.

Description

Technical Field

[0001] The present invention relates to a method and system for constructing a conduction map based on low-voltage substation big data, and belongs to the field of electric power technology. Background Art

[0002] Low-voltage substation map information, the foundational data for transparent and digital management of low-voltage substations, has long suffered from issues such as low accuracy, incompleteness, and a lack of timely updates. Without this information, analyzing and locating issues like inappropriate load monitoring and meter inaccuracies in low-voltage substations is extremely difficult, creating a series of inconveniences for refined substation management.

[0003] Currently, low-voltage substation area analysis is primarily performed through power injection or HPLC. This involves injecting power into the transformer and performing signal detection on different meter boxes to determine the relationship between the transformer and the meter box. Power injection degrades grid power quality, causing grid pollution and, in extreme cases, damage to electrical equipment, posing a safety hazard.

[0004] During meter upgrades, some meters support HPLC communication for low-voltage substation topology identification. The HPLC master device on the transformer communicates with the HPLC slave device on the meter box to determine the substation topology. HPLC is a high-speed power line carrier. In low-voltage substation power supply lines, determining the substation-transformer relationship through power line communication can lead to misjudgments and missed detections. Furthermore, this mechanism requires HPLC-based metering. In actual low-voltage substations, there are scenarios where older equipment does not support HPLC or meters are not based on HPLC, making topology identification based on HPLC impossible.

[0005] The current substation map analysis solution has shortcomings in practical applications in terms of safety and reliability. It is necessary to consider an adaptive substation map recognition solution to promote low-voltage substation topology recognition and solve problem analysis, positioning, and closed-loop problems such as load alarms and meter inaccuracies. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method and system for constructing a conduction map based on low-voltage area big data.

[0007] To solve the above technical problems, the present invention provides a method for constructing a conduction map based on low-voltage area big data, comprising:

[0008] Collect the transient voltage of the power load of the meter box in the low-voltage area;

[0009] Collect the transient voltage of transformer power load of low voltage transformer;

[0010] Calculate the correlation of power load characteristic data between meter boxes and between transformers and outgoing line cabinets based on the transient voltage of power load of meter boxes and the transient voltage of power load of transformers;

[0011] Based on the correlation of the power load characteristic data between meter boxes, the relationship between meter boxes and outgoing line cabinets is determined; based on the correlation of the power load characteristic data between transformers and outgoing line cabinets, the relationship between transformers and outgoing line cabinets is determined;

[0012] Based on the relationship between the meter box and the outgoing line cabinet, and the relationship between the transformer and the outgoing line cabinet, a complete diagram of the low-voltage substation box transformer is formed.

[0013] Furthermore, an intelligent terminal is installed at the low-voltage meter box to collect the transient voltage and transient current of the meter box power load;

[0014] A concentrator is deployed on the transformer to collect the transient voltage and current of the transformer power load.

[0015] Furthermore, the determining of the relationship between the meter box and the outlet cabinet based on the correlation of the power load characteristic data between the meter boxes includes:

[0016] Calculating a signal mean value for each meter box based on the meter box power load transient voltage of each meter box in a fixed time period of multiple low-voltage substations in a certain section frozen data, wherein the certain section frozen data includes the meter box power load transient voltage of the fixed time period;

[0017] According to the signal mean of each meter box, the first-order change sequence of the signal data of each meter box in the fixed time period is calculated;

[0018] Calculate the correlation between any two meter boxes based on the first-order change sequence of the signal data of any two meter boxes;

[0019] Obtaining the correlation between all two meter boxes in the frozen data of the certain section to form a set of correlation data, performing linear regression fitting based on the set of correlation data to form a relationship curve, calculating the correlation stability margin based on the relationship curve, and determining the conduction relationship based on the correlation stability margin;

[0020] According to the conduction relationship, the meter boxes belonging to the same low-voltage outlet cabinet are determined, and the relationship between the meter boxes and the outlet cabinet is obtained.

[0021] Furthermore, determining the conduction relationship according to the correlation stability margin includes:

[0022] If the correlation stability margin is greater than the preset threshold, it is determined that the two meter boxes are correlated; if the correlation stability margin is not greater than the preset threshold, it is determined that the two meter boxes are uncorrelated.

[0023] Furthermore, the determining of the relationship between the transformer and the outlet cabinet based on the correlation of the power load characteristic data between the transformer and the meter box includes:

[0024] Obtaining voltage data of all meter boxes under each low-voltage outlet cabinet in the frozen data of the certain section, and calculating the mean value of the voltage data of all meter boxes under each outlet cabinet;

[0025] Calculate the voltage data change sequence of each outgoing cabinet based on the mean value of the voltage data of all meter boxes under each outgoing cabinet;

[0026] Calculate the correlation between any two outgoing line cabinets based on the voltage data change sequence of any two outgoing line cabinets;

[0027] Obtaining the correlation between all two outgoing line cabinets in the frozen data of the certain section to form a set of correlation data, performing linear regression fitting based on the set of correlation data to form a relationship curve, calculating the correlation stability margin based on the relationship curve, and determining the conduction relationship based on the correlation stability margin;

[0028] According to the conduction relationship, the meter boxes belonging to the same transformer are determined, and the relationship between the transformer and the outgoing line cabinet is obtained.

[0029] A conduction map construction system based on low-voltage area big data, including:

[0030] The first acquisition module is used to collect the transient voltage of the power load of the meter box of the low-voltage area meter box;

[0031] The second acquisition module is used to collect the transient voltage of the transformer power load of the low-voltage transformer;

[0032] A calculation module, for calculating the correlation of the power load characteristic data between meter boxes and the correlation of the power load characteristic data between transformers and outgoing line cabinets based on the transient voltage of the power load of the meter box and the transient voltage of the power load of the transformer;

[0033] A determination module is used to determine the relationship between the meter box and the outgoing line cabinet based on the correlation of the power load characteristic data between the meter boxes; and to determine the relationship between the transformer and the outgoing line cabinet based on the correlation of the power load characteristic data between the transformer and the outgoing line cabinet;

[0034] The generation module is used to generate a complete diagram of the low-voltage substation box-type transformer according to the relationship between the meter box and the outgoing line cabinet, and the relationship between the transformer and the outgoing line cabinet.

[0035] A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that, when executed by a computing device, cause the computing device to perform any of the methods described.

[0036] A computing device comprising:

[0037] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described.

[0038] The beneficial effects achieved by the present invention are:

[0039] The present invention is based on the principles of voltage and current conduction and the natural data of power load, avoiding the hidden dangers of power injection and reducing the workload of manual operation. The power load characteristic data is valid data, reducing the interference of native data signals, improving information density, capturing data correlation information through first-order data, and increasing the accuracy of analysis results. Based on big data trend analysis, through curve fitting, with stability margin as the judgment basis, the probability of accidental misjudgment of small sample data is reduced. Decoupling from hardware equipment is achieved based on the conduction map, providing conditions for universal promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a diagram of the deployment of the acquisition equipment of the present invention;

[0041] Figure 2 This is a schematic diagram of the transmission of transient characteristic information of power load;

[0042] Figure 3 Provide an overall flow chart for conduction map processing;

[0043] Figure 4 It is a flowchart for table box correlation processing;

[0044] Figure 5 It is a line graph of the correlation between the load voltage and current change vectors between the meter boxes;

[0045] Figure 6 This is the result diagram of the conduction spectrum relationship of the low-voltage area. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] like Figure 3 As shown, the present invention provides a method for constructing a conduction map based on low-voltage area big data, comprising:

[0048] Step 1: Install an intelligent terminal at the meter box to collect transient voltage and current of the electrical load at the meter box.

[0049] Step 2: Based on the transient voltage and current data of the power load collected at the meter box, the power load data information identified at the meter box is obtained and reported to the master station.

[0050] Step 3: Deploy a concentrator at the transformer to collect transient voltage and current of the transformer power load, such as Figure 1 As shown;

[0051] Step 4: Based on the transient voltage and current data of the power load collected at the transformer, the load data information identified at the transformer is obtained and reported to the master station:

[0052] Step 5, such as Figure 4 As shown: Get the characteristic data of the power load in the substation for one day and calculate the mean value of the corresponding meter box signal;

[0053]

[0054] Step 6: Calculate the first-order change sequence of the signal data of one meter box in one day based on the mean value;

[0055] ΔUb1={ΔU1, ΔU2,..., ΔUi,...}

[0056] in:

[0057]

[0058] Calculate the first-order variation sequence ΔUb2 of the signal data of another meter box in the same way;

[0059] Step 7: Calculate the correlation between the two sets of signal data change vectors:

[0060] Conv(ΔUb1, ΔUb2)=E(ΔUb1, ΔUb2)-E(ΔUb1)E(ΔUb2)

[0061] get:

[0062]

[0063] The above calculation calculates the correlation of the power load characteristic data between the meter boxes, and obtains that the meter boxes belong to the same outlet cabinet, thereby obtaining the relationship between the outlet cabinet and the meter box.

[0064] Step 8: Obtain the correlation between all two meter boxes in the frozen data of a certain section (in this embodiment, data of one week is taken) to form a set of correlation data. Perform linear regression fitting based on the set of correlation data, and calculate the correlation stability margin based on the regression result. If it is greater than a certain threshold (determined based on empirical values ​​in different regions), the final correlation value is determined, such as Figure 5 As shown;

[0065] Step 9: According to the conduction relationship, it is determined that each meter box belongs to the same outlet cabinet; Figure 2 As shown, the power supply line has impedance. When a meter box generates current due to power load, there is a voltage drop in the public line corresponding to the current. This voltage drop will cause the terminal voltage of the meter box flowing through the same public line to also have a voltage drop. Simply put: the voltage drop generated on the impedance of the public transmission line will be reflected in the subsequent load.

[0066] Step 10: Calculate the meter box relationship between the transformer and the outgoing cabinet based on the conduction characteristics in a similar manner;

[0067] 1) Based on the meter boxes under the same outlet cabinet calculated in step 9), aggregate the voltage data of all meter boxes under the outlet cabinet into one equivalent value to increase data density and reduce the amount of calculation. The aggregation strategy is to take the average value:

[0068]

[0069] Among them: Ue(i): is the equivalent voltage data of the i-th outgoing cabinet;

[0070] Ub(i)(k): the voltage data of the i-th meter box in the k-th meter box under the same outlet cabinet;

[0071] n: number of aggregate meter boxes under one outlet cabinet;

[0072] 2) Calculate the voltage data change sequence of an outgoing cabinet:

[0073] ΔUe1={ΔU1, ΔU2,..., ΔUi,...}

[0074] In the same way, calculate the first-order change sequence ΔUs of the signal data collected from the transformer for one day;

[0075] 3) Calculate the correlation between the two sets of signal data change vectors:

[0076] Conv(ΔUe1, ΔUs)=E(ΔUe1, ΔUs)-E(ΔUe1)E(ΔUs)

[0077] get:

[0078]

[0079] 4) Obtaining the correlation between all transformers and outgoing line cabinets in the frozen data of the certain section to form a set of correlation data, performing linear regression fitting based on the set of correlation data, and calculating the correlation stability margin based on the regression result. If the correlation margin is greater than a certain threshold value, which is determined based on actual engineering experience, then determining the final correlation value;

[0080] The above calculation obtains the correlation between the power load characteristic data between the outgoing line cabinet and the transformer, thereby obtaining the relationship between the transformer and the outgoing line cabinet.

[0081] Step 11: Based on the relationship between the meter box and the outgoing line cabinet, and between the outgoing line cabinet and the transformer, a complete diagram of the low-voltage area box transformer can be formed, such as Figure 6 shown.

[0082] A conduction map construction system based on low-voltage area big data, including:

[0083] The first acquisition module is used to collect the transient voltage of the power load of the meter box of the low-voltage area meter box;

[0084] The second acquisition module is used to collect the transient voltage of the transformer power load of the low-voltage transformer;

[0085] A calculation module, for calculating the correlation of the power load characteristic data between meter boxes and the correlation of the power load characteristic data between transformers and outgoing line cabinets based on the transient voltage of the power load of the meter box and the transient voltage of the power load of the transformer;

[0086] A determination module is used to determine the relationship between the meter box and the outgoing line cabinet based on the correlation of the power load characteristic data between the meter boxes; and to determine the relationship between the transformer and the outgoing line cabinet based on the correlation of the power load characteristic data between the transformer and the outgoing line cabinet;

[0087] The generation module is used to generate a complete diagram of the low-voltage substation box-type transformer according to the relationship between the meter box and the outgoing line cabinet, and the relationship between the transformer and the outgoing line cabinet.

[0088] A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that, when executed by a computing device, cause the computing device to perform any of the methods described.

[0089] A computing device comprising:

[0090] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described.

[0091] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0092] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0095] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for constructing a conduction map based on low-voltage area big data, characterized in that: include: Collect the transient voltage of the power load of the meter box in the low-voltage area; Collect the transient voltage of transformer power load of low voltage transformer; Calculate the correlation of power load characteristic data between meter boxes and between transformers and outgoing line cabinets based on the transient voltage of power load of meter boxes and the transient voltage of power load of transformers; Based on the correlation of the power load characteristic data between meter boxes, the relationship between the meter boxes and the outlet cabinet is determined, including: Calculating a signal mean value for each meter box based on the meter box power load transient voltage of each meter box in a fixed time period of multiple low-voltage substations in a certain section frozen data, wherein the certain section frozen data includes the meter box power load transient voltage of the fixed time period; According to the signal mean of each meter box, the first-order change sequence of the signal data of each meter box in the fixed time period is calculated; Calculate the correlation between any two meter boxes based on the first-order change sequence of the signal data of any two meter boxes; Obtaining the correlation between all two meter boxes in the frozen data of the certain section to form a set of correlation data, performing linear regression fitting based on the set of correlation data to form a relationship curve, calculating the correlation stability margin based on the relationship curve, and determining the conduction relationship based on the correlation stability margin; Determine the meter boxes under the same low-voltage outlet cabinet based on the conduction relationship, and obtain the relationship between the meter boxes and the outlet cabinet; Based on the correlation of the power load characteristic data between the transformer and the outgoing cabinet, the relationship between the transformer and the outgoing cabinet is determined, including: Obtaining voltage data of all meter boxes under each low-voltage outlet cabinet in the frozen data of the certain section, and calculating the mean value of the voltage data of all meter boxes under each outlet cabinet; Calculate the voltage data change sequence of each outgoing cabinet based on the mean value of the voltage data of all meter boxes under each outgoing cabinet; Calculate the correlation between any two outgoing line cabinets based on the voltage data change sequence of any two outgoing line cabinets; Obtaining the correlation between all two outgoing line cabinets in the frozen data of the certain section to form a set of correlation data, performing linear regression fitting based on the set of correlation data to form a relationship curve, calculating the correlation stability margin based on the relationship curve, and determining the conduction relationship based on the correlation stability margin; Determine the meter boxes under the same transformer based on the conduction relationship, and obtain the relationship between the transformer and the outgoing line cabinet; Based on the relationship between the meter box and the outgoing line cabinet, and the relationship between the transformer and the outgoing line cabinet, a complete diagram of the low-voltage substation box transformer is formed.

2. The method for constructing a conduction map based on low-voltage area big data according to claim 1, characterized in that: Install an intelligent terminal at the low-voltage meter box to collect transient voltage and transient current of the meter box's electrical load; A concentrator is deployed on the transformer to collect the transient voltage and current of the transformer power load.

3. The method for constructing a conduction map based on low-voltage area big data according to claim 1, characterized in that: Determining the conduction relationship according to the correlation stability margin includes: If the correlation stability margin is greater than the preset threshold, it is determined that the two meter boxes are correlated; if the correlation stability margin is not greater than the preset threshold, it is determined that the two meter boxes are uncorrelated.

4. A conduction map construction system based on low-voltage area big data, characterized in that: include: . The first acquisition module is used to collect the transient voltage of the power load of the meter box of the low-voltage area meter box; The second acquisition module is used to collect the transient voltage of the transformer power load of the low-voltage transformer; A calculation module, for calculating the correlation of the power load characteristic data between meter boxes and the correlation of the power load characteristic data between transformers and outgoing line cabinets based on the transient voltage of the power load of the meter box and the transient voltage of the power load of the transformer; Determine the module for According to the correlation of the power load characteristic data between the meter boxes, the relationship between the meter boxes and the outgoing line cabinet is determined, including: calculating the signal mean of each meter box according to the transient voltage of the power load of each meter box in a fixed time period of multiple low-voltage substations in a certain section frozen data, the certain section frozen data is the transient voltage of the power load of the meter box including several fixed time periods; calculating the first-order change sequence of the signal data of each meter box in the fixed time period according to the signal mean of each meter box; calculating the correlation between any two meter boxes according to the first-order change sequence of the signal data of any two meter boxes; obtaining the correlation between all two meter boxes in the certain section frozen data to form a group of correlation data, performing linear regression fitting on the group of correlation data to form a relationship curve, and calculating the correlation stability margin based on the relationship curve, and determining the conduction relationship according to the correlation stability margin; determining the meter boxes belonging to the same low-voltage outgoing line cabinet according to the conduction relationship, and obtaining the relationship between the meter box and the outgoing line cabinet; According to the correlation of the power load characteristic data between the transformer and the outgoing line cabinet, the relationship between the transformer and the outgoing line cabinet is determined, including: obtaining the voltage data of all meter boxes under each low-voltage outgoing line cabinet in the frozen data of the certain section, and calculating the mean value of the voltage data of all meter boxes under each outgoing line cabinet; calculating the voltage data change sequence of each outgoing line cabinet according to the mean value of the voltage data of all meter boxes under each outgoing line cabinet; calculating the correlation between any two outgoing line cabinets according to the voltage data change sequence of any two outgoing line cabinets; obtaining the correlation between all two outgoing line cabinets in the frozen data of the certain section to form a group of correlation data, performing linear regression fitting on the group of correlation data to form a relationship curve, and calculating the correlation stability margin based on the relationship curve, and determining the conduction relationship based on the correlation stability margin; determining the meter boxes belonging to the same transformer according to the conduction relationship, and obtaining the relationship between the transformer and the outgoing line cabinet; The generation module is used to generate a complete diagram of the low-voltage substation box-type transformer according to the relationship between the meter box and the outgoing line cabinet, and the relationship between the transformer and the outgoing line cabinet.

5. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1 to 3.

6. A computing device, characterized in that include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for executing any one of the methods according to claims 1 to 3.

Images