A low-voltage area household-phase-kiosk-transformer identification method and system

Abstract

The application discloses a low-voltage area house-phase-box-transformer identification method and system, comprising the following steps: based on the power conservation principle, an optimization model is constructed, the similarity of the user electric meter power curve and the phase power curve of the meter box node is calculated, the user is distributed to the phase with the highest similarity, and the house-phase correlation identification is completed; the electric meter address and the collected data are read through the physical communication interface between the low-voltage measurement switch and the user electric meter. The method disclosed by the application fuses multi-source data, fully utilizes the steady-state data and address information of the electric meter, and innovatively proposes a load transient event, so that the box-transformer identification accuracy is improved through the strong features of the signal of the load transient event; the meter box node is taken as an intermediate node, the house-phase relationship identification is realized through the steady-state power conservation, the box-transformer relationship identification is realized through the load transient event, and the calculation is simple and the identification accuracy is high.

Description

Technical Field

[0001] This invention relates to the field of power system topology identification technology, specifically to a method and system for identifying low-voltage distribution area households-phase-box-transformer. Background Technology

[0002] In the daily operation and maintenance management of low-voltage distribution networks, accurately identifying the topological relationships of transformer substations (i.e., relationships between customers and transformers, between customers and phases, and between transformer boxes and meters) is a crucial foundation for achieving precise line loss analysis, anti-theft electricity inspection, rapid fault location, and intelligent operation and maintenance. However, for a long time, due to the complex structure of low-voltage distribution networks (commonly tree-like or fishbone-like topologies), the large number of customers, frequent line changes, and the lack of effective automatic identification technology, the problem of inconsistency between the actual topology and the system archive records has been very prominent.

[0003] Traditionally, topology identification relies primarily on manual on-site inspections and verification of archival records. This approach is not only labor-intensive, inefficient, and costly, but it also struggles to cope with dynamic changes in power lines, is prone to record delays or errors, and fails to meet the real-time and accurate management needs of smart distribution networks.

[0004] To overcome the limitations of manual inspection methods, various topology identification technologies have been gradually developed. With the widespread adoption of smart meters, a method based on low-density load curve data (such as 15-minute or 1-hour intervals) has been proposed. This method utilizes the law of conservation of energy or Kirchhoff's current law, employing similarity analysis or integer programming algorithms to determine the relationship between households and transformers. However, these methods are limited by data acquisition quality and transformer operating characteristics, often resulting in inconsistent accuracy and detection rates under light data loads or noise interference. Another approach utilizes HPLC communication carrier signals, analyzing information such as the carrier signal-to-noise ratio and zero-crossing phase difference between communication nodes to generate topological connections. However, this type of method suffers from electromagnetic coupling issues, leading to misidentification of adjacent transformer areas and resulting in low accuracy.

[0005] With the application of intelligent measurement switches, a method is proposed that involves installing intelligent measurement switches at transformer outgoing lines, branch boxes, meter boxes, etc., and injecting specific topology identification current signals (such as distorted currents lasting 1-1.5ms) at the switch installation nodes. The connection relationship is determined by whether each level of node receives this signal. This method is highly proactive and has high identification accuracy, but it requires the deployment of dedicated hardware equipment, and the injected power signal poses certain electrical safety hazards.

[0006] In summary, low-voltage transformer area topology identification technology is evolving from reliance on manual labor and experience towards automation and intelligence. Currently available methods face different challenges, including reliance on data quality, hardware modification costs, communication reliability, and algorithm complexity. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for identifying low-voltage distribution area households-phase-box-transformer, so as to solve the technical problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a method for identifying low-voltage distribution area households-phase-box-transformer, comprising the following steps:

[0009] S1. Based on the principle of power conservation, an optimization model is constructed to calculate the similarity between the power curve of the user's electricity meter and the power curve of each phase of the meter box node. The user is assigned to the phase with the highest similarity to complete the identification of the user-phase relationship.

[0010] S2. Read the meter address and collect data through the physical communication interface between the low-voltage measurement switch and the user's meter, and establish the binding relationship between the meter asset number and the meter box asset number through the meter address to realize the user-box relationship identification;

[0011] S3. Extract the load transient event sequence of each meter box node and transformer node, and represent each event using a multi-dimensional feature vector; use self-synchronization technology to equalize the clock deviation between sequences, and match the load transient event sequence of each meter box node with the load transient event sequence of all transformer nodes to be matched according to the multi-dimensional feature vector, and determine the box-transformer relationship and phase identifier based on the principle of the maximum number of matched events.

[0012] S4. The identification results of household-phase, household-box, and box-transformer are associated and integrated to generate a complete household-phase-box-transformer topology diagram.

[0013] Furthermore, the optimization model in step S1 takes minimizing the deviation between the power of each phase of the meter box and the sum of the power of the connected users as the optimization objective. Users are allocated in a phase combination of A, B, and C until phases A, B, and C all meet the optimization objective. The constructed optimization model can be expressed as:

[0014] ,

[0015] in, Let t be the power of the j-th phase of the meter box. Let i be the power of user i at time t. Let T be the indicator variable for the attribution relationship between user i and phase j, and T be the total number of sampling times.

[0016] Similarity calculation employs a user-by-user assignment strategy, specifically: for a user k to be assigned, it is assigned to the segment that maximizes the correlation coefficient, expressed as:

[0017] ,

[0018] in, This indicates the calculation of the correlation coefficient between two power curve sequences. Let represent the power of user k at time t, A, B, and C represent the A, B, and C phases of the power grid, respectively, and U represent the total number of users.

[0019] Furthermore, the specific steps of step S3 are as follows:

[0020] S31. Represent any load transient event L in the load transient event sequence using a five-dimensional feature vector, as follows:

[0021] ,

[0022] in, This is the start time of the load transient event. The event type for load transient events. The power after the transient event stabilizes. The wave crest factor of the transient process. The duration of the transient process of the load transient event L;

[0023] S32. By using self-synchronization technology to find matching patterns of event types in the load transient event sequences of the meter box node and the load transient event sequence of the transformer node, the clock deviation Δt is estimated. For the K pairs of matching events found, the clock deviation Δt can be estimated by the following formula:

[0024] ,

[0025] During matching, the load transient event stamps on the low-voltage smart metering switch side are corrected, specifically as follows:

[0026] ,

[0027] ,

[0028] In the above formula, This represents the start time of the load transient event for the k-th meter box node. Indicates the start time of transient events in transformer node load. This represents the start time of the load transient event at the k-th transformer node. This indicates the startup time after clock skew correction. Indicates the duration of the transient process of the corrected load transient event;

[0029] S33. The number of all successfully matched events between the load transient event sequence of the statistical table node and the load transient event sequence of the transformer node. Find the transformer for meter box B that maximizes the number of matching events. and phase The specific formula is as follows:

[0030] ,

[0031] In the above formula, and These represent the transformer and phase belonging to box B after matching, respectively; T represents the transformer before matching; and j represents the phase before matching. This represents the load transient event type vector of the meter box. This indicates the type of load transient event for phase j of the transformer.

[0032] Furthermore, the matching of the load transient events specifically involves: determining the event type. Whether they are the same, and whether the start-up time, steady-state power, transient process duration, and wave crest factor all meet the preset matching conditions, including the start-up time of the load transient event at the meter box node. The matching conditions are: ;

[0033] The matching condition for steady-state power is: ;

[0034] The matching condition for the duration of the transient process is: ;

[0035] Wave crest factor F crest The matching conditions are: ;

[0036] ,

[0037] in, This represents the peak current during a transient process. This represents the effective value of the current during a transient process. This indicates the power after the transient event in the meter box has stabilized. This indicates the power level after the transient event of the typhoon stabilizes. This indicates the duration of the transient process of the meter box load transient event L. This represents the wave crest factor of the transient process in the meter box. The wave crest factor represents the transient process of a platform change. This is a preset threshold.

[0038] The present invention also provides an identification system, comprising: an electricity meter, a low-voltage measuring switch, a transformer substation, and an edge processing unit;

[0039] The meter is a smart meter that collects steady-state power data at various time points of the user node;

[0040] The low-voltage intelligent measurement switch connects to the smart meter via the 485 interface and is equipped with a dual-mode communication module of HPLC and small wireless to transmit data to the distribution area concentrator, collect steady-state power data of each time section of the meter box node, and extract load transient events of the meter box node in real time.

[0041] The transformer substation extracts load transient events from transformer nodes in real time and receives user meter power curve data, meter box node power curve data, and load transient event sequences uploaded by low-voltage measurement switches; all data from the substation is wirelessly aggregated to the edge processing unit.

[0042] The edge processing unit collects data from electricity meters, low-voltage measurement switches, and distribution area concentrators, and executes any one of the methods described in 1-8 to achieve household-phase-box-transformer identification.

[0043] This system is a standard data acquisition device for current low-voltage distribution networks. Upgrading its functions does not require additional equipment deployment, thus improving the economic efficiency of the solution.

[0044] Beneficial effects: The method proposed in this invention integrates multi-source data, making full use of the steady-state data and address information of the electricity meter. It also innovatively proposes load transient events, improving the accuracy of transformer substation identification through the strong signal characteristics of these events. Based on the steady-state power data and load transient events generated by user electricity consumption, it eliminates the need for injected power or communication signals, thus not affecting the safe operation of the power grid and meeting the requirements for safe grid operation. Using the meter box node as an intermediate node, it identifies the relationship between the user phase and the meter box through steady-state power conservation, and identifies the relationship between the transformer substation and the meter box through load transient events. The calculation is simple and the identification accuracy is high, reaching 99.9% for normal electricity users, which is an improvement in accuracy compared to publicly available methods. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the description of the embodiments 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.

[0046] Figure 1 This is a flowchart of the identification method of the present invention;

[0047] Figure 2 This invention relates to a household-phase-box-transformer identification system and its communication method.

[0048] Figure 3 This is the load transient event type of the present invention;

[0049] Figure 4 The identification result of a 9-position meter box in area 1 of this invention;

[0050] Figure 5 The identification result of a 9-position meter box in the No. 2 transformer area of ​​this invention;

[0051] Figure 6 The identification result of a 9-position meter box in the No. 2 transformer area of ​​this invention;

[0052] Figure 7 The results of load transient event matching for meter boxes and transformers in transformer substations #1 and #3 of this invention;

[0053] Figure 8 This is a schematic diagram of the relationship between household, phase, box, and transformer in the low-voltage distribution area of ​​this invention. Detailed Implementation

[0054] 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, and 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.

[0055] like Figures 1-8 As shown, the present invention provides a method for identifying a low-voltage distribution area's household-phase-box-transformer, comprising the following steps:

[0056] S1. Household-Phase Identification: Based on the principle of power conservation, an optimization model is constructed to calculate the similarity between the power curve of the user's meter and the power curves of each phase at the meter box node. The user is then assigned to the phase with the highest similarity, thus completing the household-phase relationship identification. This optimization model aims to minimize the deviation between the power of each phase in the meter box and the sum of the power of the connected users. Users are assigned in A, B, and C phase combinations until phases A, B, and C all meet the optimization objective, i.e., the power deviation is minimized. The constructed optimization model can be expressed as:

[0057] ,

[0058] in, Let t be the power of the j-th phase of the meter box. Let i be the power of user i at time t. Let T be the indicator variable for the relationship between user i and phase j, and T be the total number of sampling times.

[0059] Similarity calculation employs a user-by-user assignment strategy, specifically: for a user k to be assigned, it is assigned to the segment that maximizes the correlation coefficient, expressed as:

[0060] ,

[0061] in, This indicates the calculation of the correlation coefficient between two power curve sequences. The power of user k at time t is represented by A, B, and C, which represent the three phases A, B, and C of the power grid, respectively. U represents the total number of users. In this embodiment, the calculation is performed daily, and the phase with the highest similarity and the corresponding user are determined after 2-3 days (which can be set according to the actual situation).

[0062] S2, Household-Box Identification: The meter address and data are read through the physical communication interface between the low-voltage measurement switch and the user's meter. The meter address and the meter asset number are uniquely associated. The binding relationship between the meter asset number and the meter box asset number is established through the meter address to realize household-box relationship identification.

[0063] S3. Box-Transformer Identification: Extract the load transient event sequence of each box node and transformer node, and represent each event using a multi-dimensional feature vector; use self-synchronization technology to equalize the clock deviation between sequences, and match the load transient event sequence of each box node with the load transient event sequences of all transformer nodes to be matched according to the multi-dimensional feature vector. Determine the box-transformer relationship and phase identifier based on the principle of the maximum number of matching events. In this embodiment, the calculation is performed daily, and the box-transformer relationship is determined based on the principle of the maximum number of matching events every 2-3 days (which can be set according to the actual situation).

[0064] This embodiment elaborates on step S3, which specifically includes the following steps:

[0065] S31. Any load transient event L in the load transient event sequence can be represented by a five-dimensional feature vector, specifically as follows:

[0066] ,

[0067] in, This is the start time of the load transient event. The event type for load transient events. The power after the transient event stabilizes. The wave crest factor of the transient process. Let L be the duration of the transient process of the load transient event. The event types of load transient events include impact type, step type, and gradual type, such as... Figure 3 As shown, where, Figure 3 (a), (b), and (c) represent the impact type, step type, and gradual change type, respectively.

[0068] S32. Clock skew Δt is estimated and corrected by matching the type consistency of load transient events using self-synchronization technology. Due to clock skew, the timestamps of the same physical event recorded on the meter box side and the transformer side differ. and There exists a fixed deviation Δt, i.e., clock deviation. Self-synchronization technology estimates clock deviation by finding matching patterns of event types in two sequences: the load transient event sequence of the meter box node and the load transient event sequence of the transformer node. For K pairs of matching events found, clock deviation Δt can be estimated by the following formula:

[0069] ,

[0070] During matching, the load transient event stamps on the low-voltage smart metering switch side are corrected, specifically as follows:

[0071] ,

[0072] ,

[0073] In the above formula, This represents the start time of the load transient event for the k-th meter box node. Indicates the start time of transient events in transformer node load. This represents the start time of the load transient event at the k-th transformer node. This indicates the startup time after clock skew correction. Indicates the duration of the transient process of the corrected load transient event;

[0074] In this embodiment, the matching of load transient events specifically involves: determining the event type. Whether they are the same, and whether the start-up time, steady-state power, transient process duration, and wave crest factor all meet the preset matching conditions;

[0075] In this embodiment, the start time of the load transient event of the meter box node The matching conditions are: ;

[0076] In this embodiment, the steady-state power matching condition is: ;

[0077] In this embodiment, the matching condition for the duration of the transient process is: ;

[0078] In this embodiment, the wave crest factor F crest The matching conditions are: .

[0079]

[0080] in, This represents the peak current during a transient process. This represents the effective value of the current during a transient process. This indicates the power after the transient event in the meter box has stabilized. This indicates the power level after the transient event of the typhoon stabilizes. This indicates the duration of the transient process of the meter box load transient event L. This represents the wave crest factor of the transient process in the meter box. The wave crest factor represents the transient process of a platform change. The preset threshold is selected based on engineering experience; in this embodiment, it is set to 0.05.

[0081] S33. The determination of the box-transformer relationship specifically involves: counting the number of successfully matched events between the load transient event sequence of the box node and the load transient event sequence of the transformer node. Find the transformer for meter box B that maximizes the number of matching events. and phase The specific formula is as follows:

[0082] ,

[0083] In the above formula, and These represent the transformer and phase to which meter box B belongs after matching. This optimal matching pair determines the transformer to which meter box B belongs. and its phase T represents the transformer before matching, and j represents the phase before matching. This represents the load transient event type vector of the meter box. This indicates the type of load transient event in phase j of the transformer, thus enabling accurate identification of the transformer-substation relationship.

[0084] S4. Joint Identification of Household-Phase-Box-Transformer Relationships: The identification results of household-phase, household-box, and box-transformer are linked and integrated to generate a complete household-phase-box-transformer topology diagram.

[0085] This embodiment uses five transformer substations in Nanjing as examples to test the recognition effect of this method. All five substations were equipped with low-voltage measurement switches, and two weeks of load transient event data were acquired to conduct a "box-transformer" relationship recognition test. Furthermore, considering the confidentiality of electricity user data, user meter data for transformers #1 and #3 were obtained from the electricity information collection system to verify the "household-phase" recognition method. Substation #1 contains 10 meter boxes and 72 users, while substation #3 contains 11 meter boxes and 81 users. Both substations have meter box configurations of 6 and 9 positions.

[0086] 1) "Household-Phase" Relationship Identification Results: Based on daily power data, the phase allocation of users within each meter box was traversed and combined. A self-optimizing algorithm was used to reduce the number of traversals and find the optimal user combination. Only 2 out of 153 users had incorrect phase identification, achieving an accuracy rate of 98.7%. The identification results for some meter boxes are shown below. Figures 4-6 As shown.

[0087] Figure 4 The identification results for a 9-position meter box in transformer substation #1 (all phases correctly identified). All three phases of this meter box belong to normal electricity users, and the phase relationships can be completely and correctly identified. Figure 4 It can also be seen that the three-phase power curves of A, B, and C are very consistent. However, there are abnormal interference points with sudden power changes in all three phases, which affect the correlation coefficient of the curves. The correlation coefficients of phase A and phase C are 0.569 and 0.783, respectively. These abnormal points also increase the power difference.

[0088] Figure 5 The data shows the identification results for a 9-position meter box in transformer substation #2 (all phases were correctly identified). The overall power consumption of this meter box is not high, and the impedance of phase B is relatively large, resulting in a significant difference between the power collected by the meter box in phase B and the sum of the power consumed by users in phase B, leading to a clear separation in the power curves. However, the consistency of the three-phase curves is excellent, with correlation coefficients all exceeding 0.9. The relationship between households and phases can be fully identified using only one day's data. This demonstrates the superiority of the proposed method. The two methods can complement or corroborate each other in different situations, improving the accuracy of identification.

[0089] Figure 6 The identification results for a 9-position meter box in transformer substation #2 (phase identification errors for 2 households). This meter box mainly serves vacant users and has generally high line impedance. The factors contributing to the deviation in the superimposed calculation are... Figure 6 It can be seen that the difference between the total power curve of phase A and the sum of user power is large, and the correlation coefficient is also lowered. Both phase B and C users are vacant users, which leads to the phase relationship identification error of 2 households in this meter box. It can be seen that even if the influencing factors are large, the method provided by this invention has a high recognition rate.

[0090] 2) Results of "box-transformer" relationship identification:

[0091] Figure 7 The table shows the load transient event matching results for meter boxes and transformers in distribution areas #1 and #3. The horizontal axis represents the serial numbers of the five transformers in the meter box, and the vertical axis represents the serial number of the meter box. The numbers in the table represent the number of matched load transient events. It is clear from the figure that the number of matches between a meter box and its associated transformer is much greater than the number of matches between non-associated transformers, indicating a 100% accuracy rate in identifying the "box-transformer" relationship.

[0092] Please refer to Figure 2The present invention also provides an identification system for implementing the above method, comprising: an electricity meter, a low-voltage measuring switch, a transformer substation, and an edge processing unit.

[0093] In this embodiment, the electricity meter is a smart meter, which collects steady-state power data of the user node at each time segment with a collection frequency of 15 minutes.

[0094] The low-voltage intelligent measurement switch connects to the smart meter via a 485 interface and is equipped with a dual-mode communication module of HPLC and small wireless to transmit data to the distribution area concentrator. It collects steady-state power data of each time section of the meter box node at a sampling frequency of 15 minutes and extracts load transient events of the meter box node in real time from the 6.4 kHz sampling data.

[0095] The transformer in the distribution area has sampling capabilities, extracting load transient events of transformer nodes in real time from 6.4kHz sampling data; it has data aggregation capabilities, accepting user meter power curve data, meter box node power curve data, and load transient event sequences uploaded by low-voltage measurement switches; and it has communication capabilities, wirelessly aggregating all data in the distribution area to the edge processing unit.

[0096] The edge processing unit collects data from electricity meters, low-voltage measurement switches, and distribution area concentrators, and executes the above method to achieve household-phase-box-transformer identification.

[0097] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0098] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A low-voltage transformer area customer-phase-kiosk-transformer identification method, characterized by, The method includes the following steps: S1. Based on the principle of power conservation, an optimization model is constructed to calculate the similarity between the power curve of the user's electricity meter and the power curve of each phase of the meter box node. The user is assigned to the phase with the highest similarity to complete the identification of the user-phase relationship. S2. Read the meter address and collect data through the physical communication interface between the low-voltage measurement switch and the user's meter, and establish the binding relationship between the meter asset number and the meter box asset number through the meter address to realize the user-box relationship identification; S3. Extract the load transient event sequence for each meter box node and transformer node, and represent each event using a multi-dimensional feature vector; By using self-synchronization technology to equalize the clock deviation between sequences, the load transient event sequence of each meter box node is matched with the load transient event sequence of all transformer nodes to be matched based on multi-dimensional feature vectors. The box-transformer relationship and phase identifier are determined based on the principle of the maximum number of matching events. S4. The identification results of household-phase, household-box, and box-transformer are associated and integrated to generate a complete household-phase-box-transformer topology diagram; The specific steps of step S3 are as follows: S31. Represent any load transient event L in the load transient event sequence using a five-dimensional feature vector, as follows: ， in, This is the start time of the load transient event. The event type for load transient events. The power after the transient event stabilizes. The wave crest factor of the transient process. The duration of the transient process of the load transient event L; S32. By using self-synchronization technology to find matching patterns of event types in the load transient event sequences of the meter box node and the load transient event sequence of the transformer node, the clock deviation Δt is estimated. For the K pairs of matching events found, the clock deviation Δt can be estimated by the following formula: ， During matching, the load transient event stamps on the low-voltage smart metering switch side are corrected, specifically as follows: ， ， In the above formula, This represents the start time of the load transient event for the k-th meter box node. Indicates the start time of transient events in transformer node load. This represents the start time of the load transient event at the k-th transformer node. This indicates the startup time after clock skew correction. Indicates the duration of the transient process of the corrected load transient event; S33. The number of all successfully matched events between the load transient event sequence of the statistical table node and the load transient event sequence of the transformer node. Find the transformer for meter box B that maximizes the number of matching events. and phase The specific formula is as follows: ， In the above formula, and These represent the transformer and phase belonging to box B after matching, respectively; T represents the transformer before matching; and j represents the phase before matching. This represents the load transient event type vector of the meter box. This indicates the type of load transient event for phase j of the transformer.

2. The identification method according to claim 1, characterized in that: The optimization model in step S1 aims to minimize the deviation between the power of each phase in the meter box and the sum of the power of the connected users. Users are allocated in a phase combination of A, B, and C until phases A, B, and C all meet the optimization objective. The constructed optimization model can be expressed as: ， in, Let t be the power of the j-th phase of the meter box. Let i be the power of user i at time t. Let T be the indicator variable for the attribution relationship between user i and phase j, and T be the total number of sampling times. Similarity calculation employs a user-by-user assignment strategy, specifically: for a user k to be assigned, it is assigned to the segment that maximizes the correlation coefficient, expressed as: ， in, This indicates the calculation of the correlation coefficient between two power curve sequences. Let represent the power of user k at time t, A, B, and C represent the A, B, and C phases of the power grid, respectively, and U represent the total number of users.

3. The identification method according to claim 1, characterized in that: The matching of the load transient events specifically involves: determining the event type. Whether they are the same, and whether the start-up time, steady-state power, transient process duration, and wave crest factor all meet the preset matching conditions, including the start-up time of the load transient event at the meter box node. The matching conditions are: ; The matching condition for steady-state power is: ; The matching condition for the duration of the transient process is: ; Wave crest factor F crest The matching conditions are: ; in, This represents the peak current during a transient process. This represents the effective value of the current during a transient process. This indicates the power after the meter box transient event stabilizes. This indicates the power level after the transient event of the typhoon stabilizes. This indicates the duration of the transient process of the meter box load transient event L. This represents the wave crest factor of the transient process in the meter box. The wave crest factor represents the transient process of a platform change. This is a preset threshold.

4. An identification system applicable to the method described in any one of claims 1-3, characterized in that, include: Electricity meters, low-voltage measurement switches, transformer substations, and edge processing units; The meter is a smart meter that collects steady-state power data at various time points of the user node; The low-voltage intelligent measurement switch connects to the smart meter via a 485 interface and is equipped with a dual-mode communication module of HPLC and small wireless to transmit data to the distribution area concentrator, collect steady-state power data of each time section of the meter box node, and extract load transient events of the meter box node in real time. The transformer substation extracts load transient events from transformer nodes in real time and receives user meter power curve data, meter box node power curve data, and load transient event sequences uploaded by low-voltage measurement switches; all data from the substation is wirelessly aggregated to the edge processing unit. The edge processing unit collects data from electricity meters, low-voltage measurement switches, and distribution area concentrators, and executes any one of the methods described in 1-3 to achieve household-phase-box-transformer identification.

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