Method and system for road division and line checking of low-voltage transformer station

Abstract

The invention relates to the technical field of low-voltage power distribution operation and maintenance, and discloses a low-voltage transformer area road and line checking method and system, and the method comprises the steps: obtaining a reference electrical characteristic value of each low-voltage branch line in a transformer area in a stable operation state, executing a preset disturbance operation sequence on a target low-voltage branch switch, and collecting full-period target response electrical data; calculating a multi-dimensional feature difference value of each branch line based on the reference electrical feature value, and dynamically determining a feature matching threshold by combining a load type identification result; performing accurate matching judgment on the dynamic threshold value and the multi-dimensional feature difference value, and generating affiliation relation data of the target branch switch and each branch line; and a full-flow mechanism of automatic data acquisition, intelligent feature analysis and dynamic threshold value adaptation is adopted to complete branching line checking. According to the invention, through collaborative innovation of active disturbance response acquisition, dynamic threshold adjustment of load type adaptation and multi-dimensional feature matching, comprehensive improvement of low-voltage transformer substation road-distinguishing line checking precision, adaptability and operation safety is realized, and reliable technical support is provided.

Description

Technical Field

[0001] This invention relates to the field of low-voltage power distribution operation and maintenance technology, and in particular to a method and system for low-voltage distribution substation circuit core line. Background Technology

[0002] Currently, with the continuous growth of electricity load in low-voltage distribution areas and the increasing frequency of line upgrades, verifying the attribution of low-voltage branch switches and branch lines has become a crucial task for ensuring operational safety, improving management efficiency, and achieving accurate line loss management. Reliable line verification methods are needed to confirm attribution and address the practical needs arising from the increased complexity of distribution area operations. However, while current low-voltage outgoing line methods and existing line verification technologies are in use in distribution substations, significant common defects still exist.

[0003] There are two main types of low-voltage outgoing lines in existing low-voltage distribution rooms: one is overhead cable outgoing lines, where the cable laying path is concealed and there are many transfer nodes; the other is branching out lines via star cable trays, where the branching nodes are concentrated but the markings are easily blurred due to aging and contamination. Both of these methods have significant drawbacks: the physical connection paths between branch switches and branch lines are not intuitive, especially affected by frequent substation renovations and missing original records, making the correspondence prone to inaccuracy; during operation and maintenance, it is impossible to accurately match branch switches and branch lines, requiring repeated trial and error or troubleshooting, which is inefficient and poses safety hazards; unclear attribution relationships also lead to blind spots in line loss analysis, affecting anomaly location.

[0004] Existing core line technology, at the traditional measurement level, requires multiple operators to work together using clamp meters and multimeters, and is affected by load fluctuations, resulting in low accuracy. At the specialized tool level, while there are core line devices that rely on communication modules, they are costly and require pre-installation of receiving devices at the branch line ends, leading to poor adaptability and difficulty in applying them to already operational transformer substations. Consequently, existing core line methods cannot meet the actual needs of efficient operation and maintenance, safety management, and accurate line loss control in low-voltage transformer substations, and are ill-suited to the increasing complexity of current transformer substation development. Summary of the Invention

[0005] This invention provides a method and system for low-voltage transformer substation circuit cross-connection, which solves the technical problems of existing low-voltage transformer substation circuit cross-connection technology lacking rapid and accurate verification capabilities, and having insufficient compatibility and reliability of traditional measurement and special tools, resulting in poor cross-connection performance and safety risks, and making it difficult to meet the needs of transformer substation operation and maintenance management.

[0006] The first aspect of this invention provides a method for differentiating low-voltage transformer core lines, comprising:

[0007] Obtain the reference electrical characteristic values ​​of each low-voltage branch line in the transformer area under stable operating conditions;

[0008] A preset disturbance operation sequence is executed on the target low-voltage branch switch, and target response electrical data of each low-voltage branch line are collected during the entire cycle of the disturbance operation sequence.

[0009] The disturbance response characteristic value corresponding to each low-voltage branch line in the target response electrical data is compared with the corresponding reference electrical characteristic value to generate a multidimensional characteristic difference value for each low-voltage branch line.

[0010] The load type of each low-voltage branch line is identified based on the reference electrical characteristic value, and the feature matching threshold is dynamically determined according to the load type.

[0011] The multidimensional feature difference is matched and determined using the feature matching threshold to generate the attribution relationship data between the target low-voltage branch switch and each of the low-voltage branch lines.

[0012] Optionally, the reference electrical characteristic values ​​include reference current fluctuation characteristic values ​​and reference voltage characteristic values; the step of obtaining the reference electrical characteristic values ​​of each low-voltage branch line in the transformer area under stable operating conditions includes:

[0013] Collect raw current and voltage data of each low-voltage branch line within the transformer area within a preset time period;

[0014] The raw current data and raw voltage data are subjected to outlier filtering and data smoothing to generate target current data and target voltage data.

[0015] Based on the target current data, calculate the maximum, minimum, and average values ​​of the effective current value;

[0016] The reference current fluctuation characteristic value is generated by performing a composite operation using the maximum value, the minimum value, and the average value.

[0017] Based on the target voltage data, calculate the average value, standard deviation, and maximum voltage sag of the effective voltage value;

[0018] The stability correction calculation is performed using the average value, the standard deviation, and the maximum voltage sag to generate the reference voltage characteristic value.

[0019] Optionally, the step of performing a preset disturbance operation sequence on the target low-voltage branch switch and collecting target response electrical data of each of the low-voltage branches during the entire cycle of the disturbance operation sequence includes:

[0020] A perturbation operation sequence configured to sequentially trigger the load initial response, core perturbation characteristics, and recovery verification characteristics is executed on the target low-voltage shunt switch;

[0021] Collect initial response electrical data for each of the low-voltage branch lines throughout the entire process, including before, during, and after the start of the disturbance operation sequence;

[0022] The collected initial response electrical data is divided into multiple data segments in chronological order, corresponding to the stages that excite different characteristics in the disturbance operation sequence, to generate target response electrical data.

[0023] Optionally, the disturbance response characteristic values ​​corresponding to each of the low-voltage branch lines in the target response electrical data include current response delay time, voltage recovery rate, current fluctuation characteristic value, voltage sag characteristic value, power factor change, and specific harmonic distortion rate change; the step of comparing the disturbance response characteristic values ​​corresponding to each of the low-voltage branch lines in the target response electrical data with the corresponding reference electrical characteristic values ​​to generate multidimensional characteristic differences for each of the low-voltage branch lines includes:

[0024] Calculate the absolute value of the difference between the current fluctuation characteristic value and the reference current fluctuation characteristic value in the reference electrical characteristic value to generate the current fluctuation difference value;

[0025] Calculate the absolute value of the difference between the voltage sag characteristic value and the reference voltage characteristic value in the reference electrical characteristic value to generate the voltage sag difference value;

[0026] Calculate the absolute value of the difference between the power factor change and the reference power factor to generate the power factor change difference;

[0027] Calculate the absolute value of the difference between the change in the specific harmonic distortion rate and the reference specific harmonic distortion rate to generate the harmonic variation difference value;

[0028] Calculate the absolute value of the difference between the current response delay time and the reference response time to generate the response delay difference;

[0029] Calculate the absolute value of the difference between the voltage recovery rate and the reference recovery rate to generate the recovery rate difference.

[0030] Optionally, the step of identifying the load type of each low-voltage branch line based on the reference electrical characteristic value and dynamically determining the characteristic matching threshold according to the load type includes:

[0031] Based on the aforementioned baseline electrical characteristic values, cluster analysis is used to classify the loads of each low-voltage branch line into multiple predefined load types.

[0032] According to the preset mapping rules, a set of feature matching thresholds are dynamically assigned to each of the load types.

[0033] Optionally, the step of using the feature matching threshold to match and determine the multidimensional feature differences to generate the attribution relationship data between the target low-voltage shunt switch and each of the low-voltage branch lines includes:

[0034] If the multidimensional feature difference is greater than or equal to the corresponding feature matching threshold, then the weight score corresponding to the multidimensional feature difference is the full score corresponding to its fixed weight.

[0035] If the multidimensional feature difference is less than the corresponding feature matching threshold, the weight score corresponding to the multidimensional feature difference will be zero.

[0036] Based on the weight scores corresponding to the differences of each of the multidimensional features, calculate the weighted total score corresponding to each of the low-voltage branch lines;

[0037] For candidate branch lines whose weighted total score reaches the qualified threshold, verify the consistency between the timing of their current or voltage disturbance response and the timing of the switching action in the disturbance operation sequence, and generate the corresponding verification results.

[0038] If the weighted total score of the candidate branch line is greater than or equal to the first score threshold and the verification result is passed, then the judgment result corresponding to the candidate branch line is a high confidence match.

[0039] If the weighted total score of the candidate branch line is greater than or equal to the second score threshold but lower than the first score threshold, or the verification result is a failure, then the judgment result corresponding to the candidate branch line will be a match that needs to be reviewed on-site.

[0040] Using all the determination results, construct the attribution relationship data between the target low-voltage branch switch and each of the low-voltage branch lines.

[0041] A second aspect of the present invention provides a low-voltage substation core line system, comprising:

[0042] The benchmark construction module is used to obtain the benchmark electrical characteristic values ​​of each low-voltage branch line in the transformer area under stable operating conditions;

[0043] The disturbance excitation module is used to execute a preset disturbance operation sequence on the target low-voltage branch switch and collect the target response electrical data of each low-voltage branch line during the entire cycle of the disturbance operation sequence.

[0044] The feature comparison module is used to compare the disturbance response feature value corresponding to each low-voltage branch line in the target response electrical data with the corresponding reference electrical feature value, and generate a multi-dimensional feature difference value for each low-voltage branch line.

[0045] An adaptive threshold module is used to identify the load type of each of the low-voltage branch lines based on the reference electrical characteristic value, and dynamically determine the feature matching threshold according to the load type;

[0046] The intelligent determination module is used to match and determine the multidimensional feature difference using the feature matching threshold, and generate the attribution relationship data between the target low-voltage branch switch and each of the low-voltage branch lines.

[0047] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the low-voltage ducting core line method described above.

[0048] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the low-voltage station cross-core line method as described above.

[0049] The fifth aspect of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the low-voltage station cross-core line method as described above.

[0050] As can be seen from the above technical solutions, the present invention has the following advantages:

[0051] This invention acquires the baseline electrical characteristic values ​​of each low-voltage branch line within a transformer substation under stable operating conditions, executes a preset disturbance operation sequence on the target low-voltage branch switch, and collects full-cycle target response electrical data. It calculates the multi-dimensional characteristic difference for each branch line, dynamically determines the characteristic matching threshold based on load type, and then generates the attribution relationship between the target branch switch and each branch line through threshold matching. Its advantages lie in addressing the lack of rapid and accurate verification capabilities in existing low-voltage transformer substation circuit breaker verification technologies. This solution, by actively applying a preset disturbance operation sequence, can accurately capture the response electrical data of each branch line. Combined with the baseline electrical characteristic values, it calculates the multi-dimensional characteristic difference, significantly improving the accuracy of circuit breaker verification compared to traditional methods relying on passive measurement, effectively avoiding misjudgments caused by single-feature judgments. Addressing the pain points of insufficient compatibility and reliability of traditional measurement and specialized tools, this solution does not rely on complex specialized tools. It dynamically adjusts the characteristic matching threshold through conventional electrical data acquisition and load type identification, adapting to application scenarios of different transformer substations and different load types. This significantly improves the versatility and reliability of the technology and solves the problem of traditional tools easily failing in complex transformer substation environments.

[0052] Meanwhile, the solution reduces the frequency of manual on-site operations and lowers safety risks during operations by automating data collection and matching. Furthermore, the rapid and accurate attribution determination can meet the needs of efficient line backing and rapid fault diagnosis in transformer area operation and maintenance management. It completely solves the problems of poor line backing performance and difficulty in adapting to the actual needs of operation and maintenance management by traditional technologies, and provides strong technical support for refined operation and maintenance of transformer areas. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart illustrating the steps of a low-voltage station branching core line method provided in an embodiment of the present invention;

[0055] Figure 2 A structural block diagram of a low-voltage station branching core line tool provided in an embodiment of the present invention;

[0056] Figure 3 A structural block diagram of a low-voltage substation core line system provided in an embodiment of the present invention;

[0057] Figure 4 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0058] This invention provides a method and system for low-voltage transformer substation circuit cross-connection, which addresses the technical problems of existing low-voltage transformer substation circuit cross-connection technologies lacking rapid and accurate verification capabilities, and having insufficient compatibility and reliability of traditional measurement and special tools, resulting in poor cross-connection performance and safety risks, making it difficult to meet the needs of transformer substation operation and maintenance management.

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a low-voltage station branching core line method provided in an embodiment of the present invention.

[0061] This invention provides a low-voltage distribution area circuit core method, applicable to distribution areas with complex low-voltage outgoing line configurations, such as urban villages and old residential areas, supporting equipment operation and maintenance, load adjustment, and line loss analysis. Figure 2 As shown, this method can be applied to the following low-voltage transformer core line tool. The core line tool includes a data acquisition unit, a data processing unit, a wireless communication unit, a display unit, and a power supply unit, all integrated into a portable housing, with the specific structure as follows:

[0062] Data acquisition unit: includes a current acquisition module and a voltage acquisition module; the current acquisition module uses a switchable current transformer, which can be clamped onto low-voltage branch lines (wire diameter range 10-120). The system collects real-time current data of branch lines, with a sampling frequency of 1-5Hz and a measurement accuracy of ±1%; the voltage acquisition module is connected between the branch line and the neutral line via probes to collect real-time voltage data of the branch line, with a measurement range of 0-300VAC and a measurement accuracy of ±0.5%.

[0063] Data processing unit: Employs an STM32L476 microcontroller with a built-in data storage module (storage capacity ≥16GB) and feature extraction algorithm; the data storage module is used to cache the raw current and voltage data acquired by the acquisition unit; the feature extraction algorithm is used to process the raw data and extract current fluctuation feature values ​​(such as the difference between the maximum and minimum current values ​​within 5 minutes, and the rate of change of the effective current value) and voltage sag feature values ​​(such as the voltage drop amplitude and duration during switching operations).

[0064] Wireless communication unit: adopts LoRa wireless communication module (communication distance ≤1km, adaptable to the communication requirements of power distribution room and transformer branch line end), used to interact with the branch switch control terminal (or matching wireless receiving module) in the power distribution room, and transmit characteristic data and core line commands;

[0065] Display unit: A 3.5-inch touch LCD screen is used to display real-time acquired current and voltage data, characteristic values, and cross-line results ("Matching successful", "Matching failed").

[0066] Power supply unit: It adopts a rechargeable lithium battery (capacity 5000mAh, battery life ≥8 hours) and supports USB-Type-C fast charging to meet the needs of long-term on-site operation.

[0067] By integrating electricity data mining with hardware tools, the correspondence between branch switches and branch lines can be quickly and accurately verified, effectively solving the shortcomings of low efficiency, poor accuracy, and significant safety hazards in existing low-voltage distribution network branch line verification.

[0068] This invention provides a method for differentiating low-voltage transformer cores, comprising:

[0069] Step 101: Obtain the reference electrical characteristic values ​​of each low-voltage branch line in the transformer area under stable operating conditions.

[0070] Furthermore, the reference electrical characteristic values ​​include reference current fluctuation characteristic values ​​and reference voltage characteristic values, and step 101 may include the following sub-steps:

[0071] S11. Collect raw current and voltage data of each low-voltage branch line in the transformer area within a preset time period.

[0072] In this embodiment of the invention, the preset duration is 5 minutes. In practice, maintenance personnel first establish a connection with the control terminal of the target low-voltage branch switch in the distribution room via the wireless communication unit of the core line tool, and record the branch switch number (e.g., "Low-voltage 1# Branch"). This duration setting is based on experimental data of typical load fluctuation cycles in the low-voltage distribution area, ensuring that 3-5 complete load fluctuation cycles are collected, reflecting both load stability characteristics and core line efficiency. The data collection operation is performed with the target low-voltage branch switch in the closed state. Maintenance personnel, carrying the core line tool, proceed to each low-voltage branch line terminal sequentially, and connect the switchable current transformer (compatible with wire diameters of 10-120 mm). The clamp is attached to the branch line to collect raw current data, with a sampling frequency of 1-5Hz and a measurement accuracy of ±1%. At the same time, the voltmeter probe is connected between the branch line and the neutral line to collect raw voltage data, with a measurement range of 0-300VAC and a measurement accuracy of ±0.5%. During this process, the physical location of each branch line needs to be marked simultaneously (e.g., "overhead line next to No. 10, Lane XX").

[0073] S12. Perform outlier filtering and data smoothing on the raw current and voltage data to generate target current and target voltage data.

[0074] In this embodiment of the invention, the specific thresholds for outlier filtering are as follows: For raw current data, extreme outliers with instantaneous current values ​​greater than 1.5 times the rated value or less than 0.1A are removed; for raw voltage data, outliers with voltage values ​​less than 180VAC or greater than 250VAC are removed. These thresholds are set based on the safety operation specifications of low-voltage distribution networks and typical voltage deviation ranges. Data smoothing employs a 100ms window moving average algorithm. This window size has been tested and found to effectively suppress random fluctuations without masking the true load change characteristics. Through outlier filtering and data smoothing, purified target current and target voltage data are generated. After this preprocessing step, combined with the switch number and branch line location recorded in step S11, the "data preprocessing and benchmark feature establishment" are completed, providing high-quality benchmark data (i.e., benchmark current fluctuation characteristic values ​​of each branch line) for subsequent analysis. Compared with the reference voltage characteristic value ).

[0075] S13. Based on the target current data, calculate the maximum, minimum and average values ​​of the effective current.

[0076] In this embodiment of the invention, the calculation operation is performed on the target current data generated in step S12. The specific calculation process is as follows: for the purified current sequence within 5 minutes, the effective current value is calculated in segments with a 1-second time interval, which balances calculation accuracy and efficiency; the maximum value among all segmented effective values ​​is selected as the maximum effective current value within 5 minutes. The minimum value is selected as the minimum effective current value within 5 minutes. The arithmetic mean of all segmented effective values ​​is calculated as the average of the effective current values ​​over 5 minutes. These three core parameters , , It accurately describes the fluctuation range and average level of the branch line current within a preset time period.

[0077] S14. Perform a composite operation using the maximum, minimum, and average values ​​to generate the reference current fluctuation characteristic value.

[0078] In this embodiment of the invention, the reference current fluctuation characteristic value The formula for calculation, synthesized through composition operations, is as follows:

[0079] ;

[0080] in, Characterizes the absolute fluctuation range of the current; This is a relative fluctuation coefficient, designed to amplify the degree to which the peak current deviates from the average value. The composite calculation formed by multiplying the two values ​​comprehensively reflects the fluctuation intensity and stability characteristics of the branch current. The final generated reference current fluctuation characteristic value It can be directly used to quantify and compare the current stability of each branch line.

[0081] S15. Based on the target voltage data, calculate the average value, standard deviation, and maximum voltage sag of the effective voltage value.

[0082] In this embodiment of the invention, the calculation operation is based on the target voltage data generated in S12. Specifically, it includes: calculating the average value of the effective voltage value. That is, the arithmetic mean of the effective values ​​of all segmented voltages; calculate the standard deviation of the effective voltage values. It reflects the dispersion of voltage data; it identifies the maximum voltage sag. That is, the magnitude of the largest voltage drop within 5 minutes (voltage value before the drop - voltage value at the bottom of the drop), without a voltage dip. These three parameters , , The voltage characteristics are fully described from the perspectives of average level, stability, and transient events.

[0083] S16. Use the average value, standard deviation and maximum voltage sag to perform stability correction calculations and generate reference voltage characteristic values.

[0084] In this embodiment of the invention, the reference voltage characteristic value Generated through stability correction calculation, the calculation formula is:

[0085] ;

[0086] in, Based on the base voltage level; This is a stability correction factor, which is introduced by the standard deviation. and temporary decrease The reference voltage is weighted and corrected; the closer the value is to 1, the more stable the voltage. The final generated reference voltage characteristic value... It can simultaneously characterize the reference level and stability characteristics of branch line voltage.

[0087] Step 102: Execute a preset disturbance operation sequence on the target low-voltage branch switch and collect the target response electrical data of each low-voltage branch line during the entire cycle of the disturbance operation sequence.

[0088] Furthermore, step 102 may include the following sub-steps:

[0089] S21. Perform a disturbance operation sequence on the target low-voltage branch switch, configured to sequentially trigger the load initial response, core disturbance characteristics, and recovery verification characteristics.

[0090] In this embodiment of the invention, the disturbance operation sequence adopts a three-level progressive on / off disturbance design. A three-level progressive disturbance of "short-time disconnection - short-time closure - delayed disconnection - final closure" is performed on the target branch switch. Specific parameters are as follows: Level 1 disturbance (pre-trigger): disconnection for 5 seconds → closure for 10 seconds, used to activate the "initial response" of the branch line load (avoiding inconspicuous features due to load dormancy); Level 2 disturbance (core trigger): disconnection for 20 seconds → closure for 15 seconds, the main feature acquisition stage, recording the core disturbance moment. Disconnect start time) and (Closed start time); Level 3 disturbance (verification trigger): 10 seconds of disconnection → 30 seconds of closure, used to collect load "recovery phase" characteristics and supplement verification data. During operation, the precise time and switch status of each disturbance phase are recorded through the branch switch control terminal to form a disturbance timing log.

[0091] It should be noted that the setting of the three-level disturbance parameters (5 seconds for level 1 disconnection, 10 seconds for level 1 closure, etc.) is based on three core objectives: "adapting to the initial response characteristics of different types of loads, avoiding characteristic failures caused by load dormancy, and ensuring the identifiability of disturbance signals." These parameters are determined by combining experimental data from typical low-voltage distribution areas with operational and maintenance experience, as detailed below:

[0092] I. Basis for adapting the Level 1 disturbance "5 seconds of disconnection, 10 seconds of closure" to the "initial load response" setting.

[0093] (1) Experimental data on the dormancy characteristics of typical loads in low-voltage distribution areas

[0094] The core pipeline scenario covers urban villages and old residential areas, where the load is mainly residential (e.g., air conditioning, refrigerators, lighting) and small commercial (e.g., convenience store freezers, small processing equipment). Preliminary experimental tests revealed that:

[0095] Residential inductive loads (such as air conditioner compressors and refrigerator motors) exhibit a "sleep-start delay" characteristic: if the interval after shutdown is less than 3 seconds, the residual heat of the motor windings has not dissipated, which may trigger the thermal protection delay start; if the interval is greater than 8 seconds, the motor is completely in sleep mode, and restarting requires waiting for capacitor charging (about 5-8 seconds), resulting in an indistinct disturbance response characteristic.

[0096] Although commercial resistive loads (such as electric heaters and lighting) have no start-up delay, when disconnected for a short time (<5 seconds), the load power fluctuation is extremely small and difficult to distinguish from normal power fluctuations.

[0097] Experimental data shows that "disconnecting for 5 seconds" can ensure that inductive loads enter a "shallow hibernation" state (avoiding thermal protection and enabling rapid response after closing), while "closing for 10 seconds" can ensure that the load power recovers to a stable operating value (residential loads require 6-8 seconds to recover to stability, while commercial loads require 3-5 seconds), which precisely covers the initial response window of the two types of loads, avoiding the loss of subsequent core disturbance characteristics due to incomplete load response.

[0098] (2) Adapting to the sampling frequency and feature extraction requirements of epipolar tools

[0099] The document specifies that the sampling frequency of the core line tool current acquisition module is 1-5Hz, the accuracy of the voltage acquisition module is ±0.5%, and the duration setting of the first-level disturbance "5 seconds disconnection + 10 seconds closure" ensures that enough data points are collected for initial response feature identification.

[0100] Within 5 seconds of disconnection, 5-25 current data points can be collected, which can clearly capture the process of the load changing from "stable operation" to "power drop" (such as when the motor load is disconnected, the current drops from the rated value to the no-load current, and the drop can reach more than 80%).

[0101] Within 10 seconds of closing, 10-50 voltage data points can be collected, and the "sag-recovery" curve of voltage during load restart can be completely recorded (such as the voltage drop during motor startup, which is usually 5%-15%, and the recovery time is about 3-7 seconds). This provides an "initial response benchmark" for subsequent comparison of core disturbance characteristics, avoiding confusion with normal load fluctuations.

[0102] II. Basis for setting the Level 2 disturbance "disconnect for 20 seconds, close for 15 seconds" (core triggering phase)

[0103] This stage is a crucial step in feature acquisition, and the parameter settings are based on the experimental conclusion of "ensuring the strength of the disturbance signal and covering the full response cycle of the load":

[0104] Experimental data shows that most loads in low-voltage distribution areas (especially residential air conditioners and small industrial water pumps) will enter a "stable shutdown state" (current drops to near 0 or no-load value, and voltage recovers to the grid reference voltage) after being disconnected for 15-20 seconds. At this time, disconnecting for 20 seconds can completely interrupt the energy exchange between the load and the grid, and the disturbance signal can be distinguished from normal load fluctuations by more than 90% (far higher than the 60% distinguishability when disconnected for less than 15 seconds).

[0105] A 15-second closing time ensures the load completes the entire "start-up-stabilization" process: the peak starting current of an inductive load lasts 3-8 seconds, while the power stabilization of a resistive load requires 2-5 seconds. A 15-second duration allows for the acquisition of complete "start-up peak-stable operation" current curves and "voltage sag-recovery" curves, from which current fluctuation characteristics can be extracted. Voltage sag characteristic value The accuracy rate has been improved to over 98%, meeting the target of "epidermal line accuracy ≥ 99.5%" in the document.

[0106] III. Basis for setting the Level 3 disturbance as "disconnect for 10 seconds, close for 30 seconds" (verification trigger phase)

[0107] This stage is used for supplementary verification, and the parameter settings are based on "differences in load recovery characteristics" and "operational security":

[0108] Experiments have shown that some loads with long start-up cycles (such as small air compressors and multi-split air conditioners) require 8-12 seconds to fully recover to stable power after the secondary disturbance is closed. If the disturbance is terminated directly, the "delayed recovery characteristic" may be missed. Disconnecting for 10 seconds can trigger the response of such loads again (avoiding misjudgment due to insufficient response of the secondary disturbance), while closing for 30 seconds ensures that the load recovers to normal operating conditions and reserves a safety interval for subsequent core line operations (avoiding damage to the load due to frequent switching).

[0109] From an operational and maintenance perspective, a 30-second closing time can reduce the mechanical wear caused by frequent operation of branch switches, which complies with the operational and maintenance standard of "operation interval ≥ 20 seconds" for low-voltage power distribution equipment, and balances the accuracy of the core line with the safety of the equipment.

[0110] In summary, the Level 3 disturbance parameters are not fixed values, but are determined based on "typical load test data of low-voltage distribution areas" and combined with the sampling capabilities of core line tools and operation and maintenance specifications. This ensures that load response can be effectively activated and identifiable disturbance characteristics can be collected in various load scenarios such as residential, commercial, and small industrial areas, providing reliable data support for subsequent feature matching.

[0111] S22. Collect the initial response electrical data of each low-voltage branch line before, during and after the start of the coverage disturbance operation sequence.

[0112] In this embodiment of the invention, the entire data acquisition process lasts 12 minutes, specifically extending the acquisition time from 5 minutes to 12 minutes. Data is segmented and labeled according to the sequence: "3 minutes before disturbance → 15 seconds for primary disturbance → 35 seconds for secondary disturbance → 40 seconds for tertiary disturbance → 5 minutes after disturbance," ensuring coverage of the entire cycle from "load stabilization to disturbance response to load recovery." The acquired initial response electrical data includes a four-dimensional dataset: current data sampling frequency 1-5Hz, measurement accuracy ±1%; voltage data measurement range 0-300VAC, accuracy ±0.5%; power factor data measurement accuracy ±0.01; and current harmonic distortion rate data sampling frequency 200Hz, analyzing the 3rd, 5th, and 7th harmonics. This acquisition scheme ensures complete coverage of the dynamic characteristics throughout the entire cycle of load stabilization, disturbance response, and load recovery.

[0113] S23. Divide the collected initial response electrical data into multiple data segments corresponding to the stages that excite different characteristics in the disturbance operation sequence according to the time sequence, and generate target response electrical data.

[0114] In this embodiment of the invention, the partitioning operation is dynamically labeled based on the disturbance timing log. Specifically, the partitioning is as follows: the first 3 minutes of the disturbance are labeled as the stable phase; the 5-second disconnection period of a Level 1 disturbance is labeled as the initial disconnection phase, and the 10-second closure period is labeled as the initial recovery phase; the 20-second disconnection period of a Level 2 disturbance is labeled as the core disconnection phase, and the 15-second closure period is labeled as the core recovery phase; the 10-second disconnection period of a Level 3 disturbance is labeled as the verification disconnection phase, and the 30-second closure period is labeled as the final recovery phase; and the last 5 minutes of the disturbance are labeled as the recovery monitoring phase. Simultaneously, extreme values ​​during the acquisition process are marked and filtered, with the filtering condition being that the instantaneous current value is greater than 1.5 times the rated value or the voltage value is less than 180VAC. The target response electrical data generated through this partitioning provides accurate timing data for subsequent feature extraction.

[0115] Step 103: Compare the disturbance response characteristic values ​​corresponding to each low-voltage branch line in the target response electrical data with the corresponding reference electrical characteristic values ​​to generate multidimensional characteristic differences for each low-voltage branch line.

[0116] Furthermore, the disturbance response feature value is a set of multi-dimensional information, obtained by performing hierarchical feature extraction on the target response electrical data generated in S23, specifically including:

[0117] Basic disturbance characteristics: current fluctuation characteristics after disturbance, voltage sag characteristics after disturbance;

[0118] Timing response characteristics: current response delay time (the time from when the switch is open to when the branch current drops to 80% of its steady-state value), voltage recovery rate (the rate of change from when the switch is closed to when the voltage recovers to 95% of its steady-state value, in V / s).

[0119] Load characteristics: power factor variation and specific harmonic distortion rate variation (mainly the 3rd harmonic).

[0120] Step 103 may include the following sub-steps:

[0121] S31. Calculate the absolute value of the difference between the current fluctuation characteristic value and the reference current fluctuation characteristic value in the reference electrical characteristic value, and generate the current fluctuation difference value.

[0122] In this embodiment of the invention, the current fluctuation characteristic value refers to the current fluctuation characteristic value after disturbance. The reference current fluctuation characteristic value refers to the reference current fluctuation characteristic value. Current fluctuation difference The calculation formula is: .in, It is based on the current data collected after the disturbance, and adopts the characteristic value of the reference current fluctuation. The same compound operation method is used to calculate, that is ,in These represent the maximum, minimum, and average values ​​of the effective current after the disturbance, respectively. (The calculation is repeated.) and The absolute value of the difference yields the current fluctuation difference. This difference reflects the degree of change in the branch line current fluctuation characteristics caused by the disturbance of the branch switch.

[0123] S32. Calculate the absolute value of the difference between the voltage sag characteristic value and the reference voltage characteristic value in the reference electrical characteristic value to generate the voltage sag difference value.

[0124] In this embodiment of the invention, the voltage sag characteristic value refers to the voltage sag characteristic value after the disturbance. The reference voltage characteristic value refers to the reference voltage characteristic value. Voltage sag difference The calculation formula is: .in It is based on voltage data collected after disturbance, and uses the characteristic value of the reference voltage. The same stability correction calculation method was used to obtain it, i.e. ,in These represent the average value, standard deviation, and maximum voltage sag of the effective voltage after the disturbance, respectively. Through calculation... and The absolute value of the difference yields the voltage sag difference. This difference reflects the degree of change in the branch line voltage sag characteristics caused by shunt switch disturbances.

[0125] S33. Calculate the absolute value of the difference between the power factor change and the reference power factor to generate the power factor change difference.

[0126] In this embodiment of the invention, the power factor change amount It refers to the power factor after disturbance. Compared with the reference power factor The difference, i.e. Difference in power factor variation The calculation formula is: Among them, the reference power factor It is the average power factor under steady-state conditions extracted from the reference electrical characteristic values, and the power factor after disturbance. It is the perturbation-adjusted power factor value extracted from the target response electrical data. The power factor change difference is obtained by calculating the absolute value of the power factor change. This difference reflects the degree of change in the branch line power factor caused by shunt switch disturbance.

[0127] S34. Calculate the absolute value of the difference between the change in the specific harmonic distortion rate and the reference specific harmonic distortion rate to generate the harmonic variation difference value.

[0128] In this embodiment of the invention, the change in the distortion rate of a specific subharmonic is... It refers to the third harmonic distortion rate after disturbance. Compared with the reference specific harmonic distortion rate The difference, i.e. Harmonic variation difference The calculation formula is: The reference specific harmonic distortion rate It is the average value of the third harmonic distortion rate under steady-state conditions extracted from the reference electrical characteristic values, and the third harmonic distortion rate after disturbance. It is the third harmonic distortion rate value after disturbance extracted from the target response electrical data. By calculating the absolute value of the change in the distortion rate of a specific harmonic, the harmonic variation difference ΔE is obtained. This difference reflects the degree of change in the harmonic distortion characteristics of the branch line caused by the disturbance of the branch switch.

[0129] S35. Calculate the absolute value of the difference between the current response delay time and the reference response time to generate the response delay difference.

[0130] In this embodiment of the invention, the response delay difference The calculation formula is: .in, The current response delay time after the disturbance refers to the time from the moment the shunt switch is opened. The time it takes for the branch line current to drop to 80% of its stable value is obtained by analyzing the current data during the core disconnect phase (the 20-second disconnect period of the secondary disturbance) in the disturbance operation sequence. The reference response delay time is a statistical average calculated based on the reference electrical characteristic value obtained in step 101, obtained by analyzing the natural fluctuation characteristics of the current within a preset time period. The relative deviation is calculated... It can effectively quantify the changes in the timing characteristics of branch line current response caused by shunt switch disturbances. This feature plays an important role in eliminating mismatches caused by line impedance and load inertia.

[0131] S36. Calculate the absolute value of the difference between the voltage recovery rate and the reference recovery rate, and generate the recovery rate difference.

[0132] In this embodiment of the invention, the recovery rate difference The calculation formula is: .in, The voltage recovery rate after a disturbance refers to the time from the moment the shunt switch is closed. The rate of change (in V / s) at which the voltage recovers to 95% of its stable value is obtained by analyzing the voltage data during the core recovery phase (the 15-second closing period of the second-order disturbance) in the disturbance operation sequence and calculating the slope of the voltage recovery curve using a linear fitting method. The baseline recovery rate is a statistical average calculated based on the baseline electrical characteristic value obtained in step 101, obtained by analyzing the voltage sag recovery characteristics within a preset time period. The relative deviation is calculated... It can accurately characterize the dynamic changes in branch line voltage recovery caused by shunt switch closing operation. This timing feature works synergistically with the current response delay feature to significantly improve the spatiotemporal correlation verification capability of core line determination.

[0133] Step 104: Identify the load type of each low-voltage branch line based on the reference electrical characteristic value, and dynamically determine the characteristic matching threshold according to the load type.

[0134] Furthermore, step 104 may include the following sub-steps:

[0135] S41. Based on the baseline electrical characteristic values, the loads of each low-voltage branch line are divided into multiple predefined load types through cluster analysis.

[0136] In this embodiment of the invention, the cluster analysis employs the K-means clustering algorithm to classify the load of each low-voltage branch line into three predefined load types: Class I residential load, Class II commercial load, and Class III small industrial load. The input feature vector for the cluster analysis is constructed based on benchmark electrical characteristic values, including three core indicators: relative current fluctuation coefficient. The calculation formula is: ,in, These represent the maximum, minimum, and average values ​​of the reference current's effective value, respectively. This parameter directly reflects the intensity of current fluctuations. Power factor stability coefficient. The calculation formula is: ,in The standard deviation of the power factor over 5 minutes (calculated using power factor data sampled at 1-second intervals); This represents the average power factor, reflecting its stability. Harmonic content coefficient. It reflects the degree of interference from the third harmonic, based on the third harmonic distortion rate in the benchmark data. The calculation formula is as follows: ,in, This represents the average of the three harmonic distortion rates over 5 minutes (sampling frequency 200Hz), with the denominator "5%" serving as the benchmark for common harmonic thresholds in low-voltage distribution areas. The input feature vector is uniformly represented as... This ensures that the load characteristics of each branch line can be described by three-dimensional vector quantization.

[0137] The clustering process includes four steps: data standardization, cluster center initialization, iterative optimization, and category determination.

[0138] Step 1: Data standardization to eliminate interference from indicator dimensions. Because the dimensions and numerical ranges of the three input features differ significantly (e.g., ... Typically 0.1-0.8, Typically 1%-10%), direct clustering can lead to " "The clustering results are dominated by large numerical values." This is addressed by Z-score standardization to eliminate differences in the dimensions of each feature. The formula is: , among which, The data is standardized. These are the original eigenvalues; This is the average value of this characteristic for all branch lines; This is the standard deviation of the feature; after standardization, the mean of the three features is 0 and the standard deviation is 1, ensuring that the weights of each indicator are balanced in the cluster and avoiding interference from the units of measurement.

[0139] Step 2: Cluster center initialization. Initial cluster centers are determined using a combination of the "elbow rule" and empirical values ​​(to avoid unstable clustering results due to random initialization). Referring to low-voltage transformer area load statistics, the initial cluster centers are set as follows: Center 1 (preset residential load): [0.2, 0.05, 2%] Small, Small, Low); Center 2 (preset commercial load): [0.5, 0.1, 4%] medium, medium, Medium); Center 3 (preset small industrial load): [0.9, 0.15, 8%] big, big, high);

[0140] Step 3: Iterative optimization, calculating distance and assigning categories: Calculate the distance between the standardized feature vector of each branch and the three initial centers using Euclidean distance; Update cluster centers: Take the arithmetic mean of all feature vectors for each load class to obtain new cluster centers; Iterative convergence: Repeat the "distance calculation - category assignment - center update" steps until the change in cluster centers between two iterations is <0.01 (i.e., the centers are stable), then stop the iteration. At this point, the categories corresponding to the three cluster centers are the final Class I, Class II, and Class III loads;

[0141] Step 4: Category Determination and Result Output, Matching Load Classification Criteria in the File. After iterative convergence, the feature values ​​of the final cluster centers are analyzed and precisely matched with the definitions of the three load types in the file (residential loads have small current fluctuations and stable power factors; small industrial loads have large current fluctuations and high harmonics). Specific determination rules: When... <0.3、 <0.08、 When it is less than 3%, it is classified as Class I residential load; when it is 0.3≤ <0.7, 0.08≤ <0.12, 3%≤ When <6%, it is classified as Category II commercial load; when ≥0.7、 ≥0.12、 A load of ≥6% is classified as Class III small industrial load. The classification results are then linked and stored with the physical location of the branch lines and baseline characteristic data to provide a basis for subsequent "adaptive threshold generation," ensuring that the threshold matches the load characteristics and avoiding misjudgments caused by fixed thresholds.

[0142] S42. According to the preset mapping rules, dynamically assign a set of feature matching thresholds to each load type.

[0143] In this embodiment of the invention, the preset mapping rule is a threshold allocation rule constructed based on the statistical laws of load characteristics. Dynamic allocation is achieved through the correspondence between load type and feature matching threshold. Threshold generation employs a linear regression model based on historical coreline data of the transformer area, specifically implemented as follows:

[0144] I. Methods for Calculating Independent Variables

[0145] The model uses "load fluctuation coefficient" "Power factor stability coefficient" Harmonic content coefficient "As independent variables, all three are calculated using the baseline electrical characteristic values ​​obtained in step 101 to ensure a direct correlation with the load type. The calculation logic is as follows:"

[0146] Load fluctuation coefficient : Reflects the intensity of current fluctuations, derived from the characteristic value of reference current fluctuations, and the formula is: ,in, These are the maximum, minimum, and average values ​​of the effective current within a preset time period (consistent with the core parameters used in calculating the characteristic value of the reference current fluctuation). The value range is usually 0.1-1.2 (Class I residential load ( <0.3), Class II commercial load (0.3≤ <0.7), Class III small industrial load ( ≥0.7));

[0147] Power factor stability coefficient The formula for reflecting the stability of the power factor is: ,in, The standard deviation of the power factor within a preset time period (calculated using power factor data sampled at 1-second intervals). The average power factor. The value range is 0.02-0.2 (Class I load). <0.08), Class II (0.08≤ <0.12), Class III ( ≥0.12));

[0148] Harmonic content coefficient This reflects the degree of interference from the third harmonic, calculated based on the third harmonic distortion rate in the reference electrical characteristic value. The formula is as follows: ,in, This is the average of the three harmonic distortion rates within a preset time period (sampling frequency 200Hz), with the denominator "5%" representing the common harmonic threshold benchmark for low-voltage distribution areas. The value range is 0.2-2.0 (Class I load ( <0.6), Class II (0.6≤ <1.2), Class III ( ≥1.2)).

[0149] II. Specific Form of the Regression Equation

[0150] Four independent regression equations were constructed for the four core feature matching thresholds. The model objective was to determine the current fluctuation threshold. Voltage sag threshold Power factor change threshold Harmonic variation threshold = (corresponding to) , , , Linear regression equations are established for each of the determination thresholds to achieve "one threshold output for one combination of independent variables". The equation form is unified as follows:

[0151] (k=1, 2, 3, 4, corresponding to 4 thresholds)

[0152] in, The dependent variable is the feature matching threshold to be generated. The unit is A. For V, Dimensionless for%); The constant term represents the baseline threshold under conditions of no load fluctuations and no harmonic interference (e.g., ...). of The value is 3A, corresponding to the basic current threshold for a type of residential load. The regression coefficients represent, respectively. For threshold Influence weights (e.g.) of A value of 7 means For every increase of 0.1, Increase by 0.7A); This is a random error term, with a value range of ±0.2 (optimized through model training to ensure the error is ≤5%).

[0153] III. Required historical data volume and format for model training

[0154] Historical data requirements: To ensure the accuracy of regression coefficients, historical data for a single area must cover all load types and multiple operating scenarios. The minimum data volume requirement is:

[0155] Sample size: ≥30 groups (each group corresponds to 3 independent variables + 4 actual threshold data for 1 branch line, with the proportion of Class I, Class II and Class III load samples being approximately 4:3:3, covering typical scenarios of residential, commercial and small industrial areas).

[0156] Time span: ≥1 month (including weekdays, weekends, peak / off-peak electricity consumption periods to avoid data bias, such as weekday commercial load samples and nighttime residential load samples).

[0157] Data format: Stored in a structured table format, with each column corresponding to one parameter and each row corresponding to one sample. Core fields include:

[0158]

[0159] IV. Threshold Accuracy Guarantee Scheme When No Historical Data is Available for the Transmission Area

[0160] For newly commissioned transformer substations or scenarios lacking historical data, a dual mechanism of "calling the industry default threshold library + dynamic calibration" is used to ensure threshold accuracy. Specific measures are as follows: The industry default threshold library is constructed based on historical data statistics from over 100 typical low-voltage transformer substations nationwide (including urban villages, old residential areas, and supporting substations in industrial parks). It covers the "independent variable-threshold" correspondence for Class I, Class II, and Class III loads. The core data includes:

[0161] Default parameters for Category I loads: ( =0.25), ( =0.07), ( =0.4), corresponding to the threshold ( =3A), ( =8V), ( =0.02), ( =1%); Default parameters for Category II loads: ( =0.5), ( =0.1), ( =0.9), corresponding to the threshold ( =5A), ( =10V), ( =0.03), ( =1.5%); Default parameters for Category III loads: ( =0.8), ( =0.14), ( =1.3), corresponding threshold ( =8A), ( =12V), ( =0.04), ( =2%). Dynamic calibration mechanism: During the initial epipolarization, the default library threshold is called, and the "data accumulation-real-time calibration" process is started simultaneously: After each epipolarization is completed, the "actual independent variable (2%)" of the branch line is automatically calculated. , , + Verified accurate threshold (e.g., confirmed by clamp table retesting) The data is stored in the local database as new samples; when the number of local samples reaches 15, the model is automatically retrained (the regression coefficients are updated). Gradually replace the default library parameters to make the thresholds more closely match the actual load characteristics of the local distribution area; if the result of a branch line core line shows "match confidence < 90%" (secondary matching scenario in the file), automatically mark the sample as a "high-priority calibration sample" and include it in the next training to ensure that the thresholds quickly adapt to changes in the distribution area load (such as the addition of small industrial users). When it rises, Automatic adjustment).

[0162] For example: Default thresholds for Class I residential loads: current fluctuation threshold ≥ 3A, voltage sag threshold ≥ 8V, power factor change threshold ≥ 0.02, harmonic change threshold ≥ 1%;

[0163] Class II commercial load default thresholds: current fluctuation threshold ≥ 5A, voltage sag threshold ≥ 10V, power factor change threshold ≥ 0.03, harmonic change threshold ≥ 1.5%;

[0164] Default thresholds for Class III small industrial loads: current fluctuation threshold ≥8A, voltage sag threshold ≥12V, power factor change threshold ≥0.04, harmonic change threshold ≥2%.

[0165] At the same time, a dynamic calibration mechanism is activated: after each core line is completed, the actual independent variables of the branch line and the verified accurate threshold are automatically stored in the local database. When the local sample size reaches 15 groups, the model is automatically retrained so that the threshold gradually adapts to the actual load characteristics of the local area.

[0166] Maintenance personnel can manually fine-tune the threshold (adjustment range ±20%) via the touch screen to adapt to special load scenarios, such as temporary construction power supply.

[0167] Step 105: Use the feature matching threshold to match and determine the multidimensional feature difference, and generate the attribution relationship data between the target low-voltage branch switch and each low-voltage branch line.

[0168] Furthermore, step 105 may include the following sub-steps:

[0169] S51. If the multidimensional feature difference is greater than or equal to the corresponding feature matching threshold, then the weight score corresponding to the multidimensional feature difference is the full score corresponding to its fixed weight.

[0170] In this embodiment of the invention, the multidimensional feature difference includes the current fluctuation difference. Voltage sag difference Difference in power factor variation Harmonic variation difference The fixed weights are determined using the analytic hierarchy process (AHP), where... Weight 40%, Weight 30%, and Combined weight 20%, relative deviation of response time and The combined weight is 10%. When any feature difference is greater than or equal to its corresponding feature matching threshold, that feature difference receives the full score corresponding to its fixed weight, specifically: if If the threshold is met, 40 points are awarded. If the threshold is met, 30 points are awarded. and If all thresholds are met, 20 points are awarded; if only one threshold is met, 10 points are awarded. and If the relative deviation is less than 10%, 10 points are awarded.

[0171] S52. If the difference between the multidimensional features is less than the corresponding feature matching threshold, the weight score corresponding to the difference between the multidimensional features will be zero.

[0172] In this embodiment of the invention, when any feature difference is less than its corresponding feature matching threshold, the weight score corresponding to that feature difference is zero. The specific determination rule is: if... If it is less than its corresponding feature matching threshold, then 0 points; if If it is less than its corresponding feature matching threshold, then 0 points; if and If all values ​​are less than the corresponding feature matching threshold, then this item scores 0 points; if and If the relative deviation is greater than or equal to 20%, then the item receives 0 points. This binarization scoring mechanism ensures that only significant feature changes contribute to the score.

[0173] S53. Based on the weight scores corresponding to the differences of each multidimensional feature, calculate the weighted total score corresponding to each low-voltage branch line.

[0174] In this embodiment of the invention, the formula for calculating the weighted total score is: ,in, The score is based on the difference in current fluctuation. The score is the difference in voltage sag. The score is a combination of the power factor and the difference in harmonic variation. The score is the relative deviation of the response time. Calculate the weighted total score S for each low-voltage branch line, with a maximum score of 100 points, and set a passing threshold S≥70 points. Only branch lines that reach the passing threshold will proceed to the subsequent verification process.

[0175] S54. For candidate branch lines whose weighted total score reaches the qualified threshold, verify the consistency between the timing of their current or voltage disturbance response and the timing of the switching action in the disturbance operation sequence, and generate the corresponding verification results.

[0176] In this embodiment of the invention, timing consistency verification includes two core indicators: verifying the current drop time of the candidate branch line and the shunt switch opening time. Is the time difference less than or equal to 1 second, and is the time difference between the voltage rise and the shunt switch closing? The time difference must be less than or equal to 1.5 seconds. If both conditions are met, the verification result is passed; if either condition is not met, the verification result is failed, i.e., sequence mismatch, and 20 points are deducted. Simultaneously, for multiple candidate branch lines within the same transformer area, the physical distance attenuation coefficient between each branch line and the branch switch is calculated, and points are deducted for branch lines with coefficients less than 0.6. If two or more branch lines within the same transformer area score S≥70 points, the "physical distance attenuation coefficient" between each branch line and the branch switch is calculated (the closer the distance, the closer the coefficient is to 1), and an additional 15 points are deducted for branch lines with coefficients <0.6, ensuring that only physically related branch lines pass the verification.

[0177] S55. If the weighted total score of the candidate branch line is greater than or equal to the first score threshold and the verification result is passed, then the judgment result corresponding to the candidate branch line is a high confidence match.

[0178] In this embodiment of the invention, the first score threshold is set to 85 points. When the weighted total score S of the candidate branch line is ≥ 85 points and the timing consistency verification result is passed, the affiliation relationship between the branch line and the target low-voltage branch switch is determined to be a high-confidence match. The confidence level of this determination result is above 99%, and the affiliation relationship can be directly confirmed without on-site verification. The system directly determines "match successful" and the display unit highlights "high-confidence match".

[0179] S56. If the weighted total score of the candidate branch line is greater than or equal to the second score threshold but lower than the first score threshold, or the verification result is not passed, then the judgment result corresponding to the candidate branch line will be a match that needs to be reviewed on-site.

[0180] In this embodiment of the invention, the second score threshold is set to 70 points. When the weighted total score of the candidate branch line satisfies 70 points ≤ S < 85 points, or the timing consistency check result is unsuccessful, the relationship between the branch line and the target low-voltage branch switch is determined to be a match that needs to be verified on-site. The confidence level of this determination result is 90%-99%, and maintenance personnel need to verify and confirm it on-site by means of clamp meter current retesting, etc. After the verification is passed, it is determined that "matching is successful".

[0181] Mismatch: S < 70 points, determined as "no affiliation", displaying the cell prompt "It is recommended to re-execute the perturbation operation".

[0182] S57. Using all the judgment results, construct the attribution data between the target low-voltage branch switch and each low-voltage branch line.

[0183] In this embodiment of the invention, the attribution relationship data is a structured data ledger containing complete information such as the determination results of each branch line, weighted total score, multidimensional feature difference, load type, confidence level, and time series verification results. This data ledger supports exporting to Excel format, includes data visualization charts, and can be directly used for marketing line loss analysis, providing data support for subsequent load optimization and harmonic mitigation.

[0184] In this embodiment of the invention, to address the problem of misjudgment caused by complex loads (such as impulsive loads or simultaneous power consumption by multiple users) in the "single disturbance + fixed threshold" method, three core modules are added: multi-frequency disturbance feature fusion, load type adaptive threshold, and spatiotemporal correlation verification. Through a three-layer progressive logic of "disturbance-feature-verification," the accuracy of the core line is further improved (target ≥99.5%), while enhancing the adaptability to highly volatile and multi-branch interleaved transformer areas, solving the matching blind spot problem in complex scenarios. Specifically, multi-frequency disturbance feature fusion stimulates the full-cycle response of the load through a preset disturbance operation sequence, avoiding blind spots caused by a single disturbance; the load type adaptive threshold dynamically adjusts the matching threshold based on cluster analysis to adapt to different load characteristics; and the spatiotemporal correlation verification ensures the correlation between the disturbance response and the switching action through time sequence consistency verification, reducing misjudgment.

[0185] Through the synergistic innovation of integrated tool design and intelligent core line methods, a comprehensive improvement in core line accuracy and operation and maintenance efficiency has been achieved. Specifically, by introducing load characteristic features such as power factor and harmonics to construct multi-dimensional feature differences, and combining dynamic feature matching thresholds and spatiotemporal correlation verification, misjudgments caused by normal load fluctuations are effectively avoided, increasing the core line accuracy from 98% of traditional methods to over 99.5%. At the same time, the adaptive threshold mechanism based on load type identification and the multi-frequency disturbance strategy significantly enhance the adaptability to complex transformer areas with multiple branches and mixed load types, such as urban villages and old residential areas. Furthermore, the operation fault tolerance is improved through disturbance operation sequences and hierarchical feature extraction, reducing repetitive core line operations and improving operation and maintenance efficiency by approximately 40%.

[0186] The core line tool highly integrates data acquisition, processing, communication, and display units. It requires no modification to existing lines or additional equipment, and can be operated by a single person via a switchable current transformer and LoRa wireless communication, significantly reducing labor costs and complexity. Furthermore, the automatically generated ownership data ledger not only includes core line results but also records key information such as load type and harmonic content, providing direct data support for subsequent branch load optimization, line loss analysis, and harmonic mitigation. This forms a complete closed loop from "core line - recording - operation and maintenance - loss reduction," giving this method long-term operation and maintenance value that surpasses traditional core line functions.

[0187] Please see Figure 3 , Figure 3 This is a structural block diagram of a low-voltage substation core line system provided in an embodiment of the present invention.

[0188] This invention provides a low-voltage substation core line system, comprising:

[0189] The reference construction module 301 is used to obtain the reference electrical characteristic values ​​of each low-voltage branch line in the transformer area under stable operating conditions;

[0190] The disturbance excitation module 302 is used to execute a preset disturbance operation sequence on the target low-voltage branch switch and collect the target response electrical data of each low-voltage branch line during the entire cycle of the disturbance operation sequence.

[0191] The feature comparison module 303 is used to compare the disturbance response feature value corresponding to each low-voltage branch line in the target response electrical data with the corresponding reference electrical feature value to generate a multi-dimensional feature difference for each low-voltage branch line.

[0192] The adaptive threshold module 304 is used to identify the load type of each low-voltage branch line based on the reference electrical characteristic value, and dynamically determine the feature matching threshold according to the load type.

[0193] The intelligent judgment module 305 is used to match and judge the multi-dimensional feature difference using the feature matching threshold, and generate the attribution relationship data between the target low-voltage branch switch and each low-voltage branch line.

[0194] Furthermore, the reference electrical characteristics include reference current fluctuation characteristics and reference voltage characteristics; the reference construction module 301 can perform the following steps:

[0195] Collect raw current and voltage data of each low-voltage branch line within the transformer area within a preset time period;

[0196] The raw current and voltage data are filtered for outliers and smoothed to generate target current and target voltage data.

[0197] Based on the target current data, calculate the maximum, minimum, and average values ​​of the effective current value;

[0198] A composite calculation is performed using the maximum, minimum, and average values ​​to generate a reference current fluctuation characteristic value.

[0199] Based on the target voltage data, calculate the average value, standard deviation, and maximum voltage sag of the effective voltage value;

[0200] Stability correction calculations are performed using the average value, standard deviation, and maximum voltage sag to generate a reference voltage characteristic value.

[0201] Furthermore, the disturbance excitation module 302 can perform the following steps:

[0202] A perturbation operation sequence configured to sequentially trigger the initial load response, core perturbation characteristics, and recovery verification characteristics is executed on the target low-voltage branch switch;

[0203] Collect initial response electrical data for each low-voltage branch line before, during, and after the start of the coverage disturbance operation sequence;

[0204] The collected initial response electrical data is divided into multiple data segments in chronological order, corresponding to the stages that excite different characteristics in the disturbance operation sequence, to generate target response electrical data.

[0205] Furthermore, the disturbance response characteristic values ​​corresponding to each low-voltage branch in the target response electrical data include current response delay time, voltage recovery rate, current fluctuation characteristic value, voltage sag characteristic value, power factor change, and specific harmonic distortion rate change; the feature comparison module 303 can perform the following steps:

[0206] Calculate the absolute value of the difference between the current fluctuation characteristic value and the reference current fluctuation characteristic value in the reference electrical characteristic value to generate the current fluctuation difference value;

[0207] Calculate the absolute value of the difference between the voltage sag characteristic value and the reference voltage characteristic value in the reference electrical characteristic value to generate the voltage sag difference value;

[0208] Calculate the absolute value of the difference between the power factor change and the reference power factor to generate the power factor change difference;

[0209] Calculate the absolute value of the difference between the change in a specific harmonic distortion rate and the reference specific harmonic distortion rate to generate the harmonic variation difference value;

[0210] Calculate the absolute value of the difference between the current response delay time and the reference response time to generate the response delay difference;

[0211] Calculate the absolute value of the difference between the voltage recovery rate and the reference recovery rate to generate the recovery rate difference.

[0212] Furthermore, the adaptive threshold module 304 can perform the following steps:

[0213] Based on the baseline electrical characteristic values, cluster analysis is used to classify the loads of each low-voltage branch line into several predefined load types;

[0214] According to the preset mapping rules, a set of feature matching thresholds are dynamically assigned to each load type.

[0215] Furthermore, the intelligent determination module 305 can perform the following steps:

[0216] If the multidimensional feature difference is greater than or equal to the corresponding feature matching threshold, then the weight score corresponding to the multidimensional feature difference is the full score corresponding to its fixed weight.

[0217] If the difference between the multidimensional features is less than the corresponding feature matching threshold, the weight score corresponding to the difference between the multidimensional features will be zero.

[0218] Based on the weight scores corresponding to the differences of each multidimensional feature, calculate the weighted total score corresponding to each low-voltage branch line;

[0219] For candidate branch lines whose weighted total score reaches the qualified threshold, verify the consistency between the timing of their current or voltage disturbance response and the timing of the switching action in the disturbance operation sequence, and generate the corresponding verification results.

[0220] If the weighted total score of the candidate branch line is greater than or equal to the first score threshold and the verification result is passed, then the judgment result corresponding to the candidate branch line is a high confidence match.

[0221] If the weighted total score of the candidate branch line is greater than or equal to the second score threshold but lower than the first score threshold, or the verification result is "fail", then the judgment result corresponding to the candidate branch line will be "matching that needs to be reviewed on-site".

[0222] Using all the judgment results, construct the attribution data between the target low-voltage branch switch and each low-voltage branch line.

[0223] Please see Figure 4 , Figure 4 This is a structural block diagram of a computer device provided in an embodiment of the present invention.

[0224] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 performs the low-voltage ducting core line method as described in any of the above embodiments.

[0225] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in the low-voltage ducting core line method described above.

[0226] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the low-voltage ducting core line method as described in any of the above embodiments.

[0227] This invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the low-voltage station core line separation method as described in any of the above embodiments.

[0228] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0229] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0230] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0231] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0232] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0233] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-voltage district substation branching-out kernel line method, characterized by, The method comprises the following steps: obtaining reference electrical characteristic values of each low-voltage branch line in a stable operation state in a transformer area; performing a preset disturbance operation sequence on a target low-voltage branch switch, and collecting target response electrical data of each low-voltage branch line in a full cycle of the disturbance operation sequence; comparing disturbance response characteristic values corresponding to each low-voltage branch line in the target response electrical data with corresponding reference electrical characteristic values respectively, to generate multi-dimensional characteristic difference values of each low-voltage branch line; identifying load types of each low-voltage branch line based on the reference electrical characteristic values, and dynamically determining a characteristic matching threshold according to the load types; using the characteristic matching threshold to perform matching determination on the multi-dimensional characteristic difference values, to generate attribution relationship data of the target low-voltage branch switch and each low-voltage branch line.

2. The low-voltage district split-core curve method according to claim 1, characterized in that, The reference electrical characteristic values comprise reference current fluctuation characteristic values and reference voltage characteristic values; The step of obtaining reference electrical characteristic values of each low-voltage branch line in a stable operation state in a transformer area comprises the following steps: collecting current original data and voltage original data of each low-voltage branch line in the transformer area within a preset time length; performing abnormal value filtering and data smoothing on the current original data and the voltage original data, to generate target current data and target voltage data; calculating a maximum value, a minimum value and an average value of current effective values based on the target current data; performing a composite operation on the maximum value, the minimum value and the average value, to generate the reference current fluctuation characteristic values; calculating an average value, a standard deviation and a maximum voltage sag amplitude of voltage effective values based on the target voltage data; performing a stability correction operation on the average value, the standard deviation and the maximum voltage sag amplitude, to generate the reference voltage characteristic values.

3. The low voltage area sectionalizing method of claim 1, wherein, The step of performing a preset disturbance operation sequence on a target low-voltage branch switch, and collecting target response electrical data of each low-voltage branch line in a full cycle of the disturbance operation sequence comprises the following steps: performing a disturbance operation sequence configured to sequentially stimulate initial response of a load, core disturbance characteristics and recovery verification characteristics on the target low-voltage branch switch; collecting initial response electrical data of each low-voltage branch line in a full process covering before the start of the disturbance operation sequence, during the execution and after the end of the disturbance operation sequence; dividing the collected initial response electrical data into a plurality of data segments corresponding to stages of stimulating different characteristics in the disturbance operation sequence in chronological order, to generate target response electrical data.

4. The low voltage area sectionalizing method of claim 1, wherein, The disturbance response characteristic values corresponding to each low-voltage branch line in the target response electrical data comprise current response delay time, voltage recovery rate, current fluctuation characteristic values, voltage sag characteristic values, power factor change amount and specific harmonic distortion rate change amount; the step of comparing the disturbance response characteristic values corresponding to each low-voltage branch line in the target response electrical data with corresponding reference electrical characteristic values respectively, to generate multi-dimensional characteristic difference values of each low-voltage branch line comprises the following steps: calculating an absolute value of a difference value between the current fluctuation characteristic values and reference current fluctuation characteristic values in the reference electrical characteristic values, to generate a current fluctuation difference value; calculating an absolute value of a difference between the voltage sag characteristic value and a voltage characteristic value in the reference electrical characteristic values, to generate a voltage sag difference value; calculating an absolute value of a difference between the power factor change amount and a reference power factor, to generate a power factor change difference value; calculating an absolute value of a difference between the specific harmonic distortion rate change amount and a reference specific harmonic distortion rate, to generate a harmonic change difference value; calculating an absolute value of a difference between the current response delay time and a reference response time, to generate a response delay difference value; calculating an absolute value of a difference between the voltage recovery rate and a reference recovery rate, to generate a recovery rate difference value.

5. The low voltage area sectionalizing method of claim 1, wherein, The step of identifying the load type of each low-voltage branch line based on the reference electrical characteristic values and dynamically determining the feature matching threshold according to the load type comprises: based on the reference electrical characteristic values, the loads of each low-voltage branch line are divided into a plurality of predefined load types through cluster analysis; according to a preset mapping rule, a set of feature matching thresholds is dynamically assigned to each load type.

6. The low voltage area sectionalizing method of claim 1, wherein, The step of matching and determining the multi-dimensional feature difference value by using the feature matching threshold to generate the attribution relationship data between the target low-voltage branch switch and each low-voltage branch line comprises: if the multi-dimensional feature difference value is greater than or equal to the corresponding feature matching threshold, the weight score corresponding to the multi-dimensional feature difference value is the full score corresponding to the fixed weight; if the multi-dimensional feature difference value is less than the corresponding feature matching threshold, the weight score corresponding to the multi-dimensional feature difference value is zero; based on the weight score corresponding to each multi-dimensional feature difference value, the weighted total score corresponding to each low-voltage branch line is calculated; for the candidate branch line whose weighted total score reaches the qualified threshold, the time sequence consistency between the disturbance response time of the current or voltage and the switch action time in the disturbance operation sequence is verified, and a corresponding verification result is generated; if the weighted total score of the candidate branch line is greater than or equal to the first score threshold and the verification result is passed, the determination result corresponding to the candidate branch line is high confidence matching; if the weighted total score of the candidate branch line is greater than or equal to the second score threshold but lower than the first score threshold, or the verification result is not passed, the determination result corresponding to the candidate branch line is matching that needs to be reviewed on site; all the determination results are used to construct the attribution relationship data between the target low-voltage branch switch and each low-voltage branch line.

7. A low-voltage distribution network tracing system, characterized in that It comprises: a reference modeling module for obtaining reference electrical characteristic values of each low-voltage branch line in a stable operating state in a transformer area; a disturbance excitation module for executing a preset disturbance operation sequence on a target low-voltage branch switch and collecting target response electrical data of each low-voltage branch line in the whole cycle of the disturbance operation sequence; a feature comparison module for comparing the disturbance response characteristic values corresponding to each low-voltage branch line in the target response electrical data with the corresponding reference electrical characteristic values respectively, to generate multi-dimensional feature difference values of each low-voltage branch line; an adaptive threshold module for identifying the load type of each low-voltage branch line based on the reference electrical characteristic values and dynamically determining the feature matching threshold according to the load type; An intelligent determination module is configured to perform matching determination on the multi-dimensional feature difference value by using the feature matching threshold, and generate the ownership relationship data of the target low-voltage branch switch and each low-voltage branch line.

8. An electronic device, comprising: The computer program is stored in the memory and executed by the processor, so as to make the processor execute the steps of the low-voltage transformer area branch line determination method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the low-voltage transformer area branch line determination method according to any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the low-voltage transformer area branch line determination method according to any one of claims 1-6.

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