Low-voltage distribution network user-transformer relation checking method, device and system and medium

Abstract

The invention provides a low-voltage distribution network user-transformer relation checking method, device and system and a medium. The method comprises the following steps: extracting a load event according to load waveforms of a user and a transformer; extracting load characteristics according to the extracted load events; character processing is carried out on the extracted load characteristics, and the load characteristics are sequentially arranged according to a pre-sequence to obtain a load event character string sequence of the user to be identified; calculating the longest common subsequence of the load event character string sequence of the user to be identified and the load event character string sequence of each transformer, calculating a time deviation entropy value of the user to be identified corresponding to each transformer according to the longest common subsequence, and determining the transformer to which the user to be identified belongs according to the time deviation entropy value; and checking the determined transformer to which the user to be identified belongs and the transformer to which the user to be identified belongs recorded by the system file. According to the invention, the problems of additional measurement equipment and high requirement on data precision in the prior art can be solved, and the topology identification accuracy is improved.

Description

Technical Field

[0001] The invention relates to the technical field of distribution network, and in particular to a method, device, system and medium for verifying the relationship between household transformers in a low-voltage distribution network. Background Art

[0002] The relationship between household transformers is the most important basic archive information in low-voltage substations. It is widely used in load management, substation mutual assistance, power flow calculation, state estimation, lean line loss management, fault location, etc. However, the archive maintenance work of clearing up the front and messing up the back has long been a governance problem that has plagued power grid companies. With the upgrading of the lean management level of substations and the transformation of digital operation and maintenance, traditional manual maintenance modes such as "power outage verification", "on-site experience observation" and "topology identification tools" are time-consuming and labor-intensive, and the update speed cannot meet the application analysis requirements. For example, the analysis and diagnosis of daily line losses, the optimal allocation of open capacity, and the coordinated autonomy of substation sources and loads all require accurate real-time household transformer relationships. In order to solve this engineering application problem, the automatic identification technology of household transformer relationships has gradually become a research hotspot. From the perspective of technical principles, the existing household transformer automatic identification technology is mainly divided into two categories. One is the signal matching method, including communication signal matching and injection power signal matching; the other is the data-driven method, which is based on the massive measurement data collected by AMI, according to the analysis of the circuit operation mechanism, and takes voltage correlation and energy conservation characteristics as breakthroughs, and combines machine learning methods for modeling research.

[0003] The signal matching method is mainly an engineering method, with less academic research. This type of method has high recognition efficiency but requires equipment installation, and the engineering construction cost is high. In terms of communication signal matching, with the help of the HPLC communication signal of the low-voltage area carrier communication terminal, household change and phase judgment are carried out through communication signal label matching, communication message characteristics, signal transmission time measurement and power frequency signal zero-crossing detection. This type of method is only applicable to HPLC full coverage communication coverage areas, and is susceptible to electromagnetic coupling and noise interference. In addition, it is not widely used in engineering because it affects the success rate of AMI system acquisition. In the study of power signal injection method, a power signal of a specific interharmonic is generated by a signal generator for topological matching. This method has a large amount of engineering implementation, high cost, safety hazards, and will fail for fishbone power grids with complex structures.

[0004] For the data-driven method, the AMI system has accumulated a large amount of user-side load measurement data, and applied machine learning, big data analysis and other methods to explore the non-metering value of existing metering data, which is of great value for engineering applications. The academic community has carried out extensive research. One type is based on the spatiotemporal distribution and correlation characteristics of node voltage, and the household transformer affiliation is judged through correlation analysis such as Pearson correlation coefficient and grey correlation degree; in addition, household transformer relationship identification is essentially to solve the classification problem. Other studies introduce clustering algorithms into it. This type of method mainly conducts research from two aspects: effective voltage feature extraction method and clustering algorithm optimization. However, it has high requirements on the quality of voltage data. Strict data screening conditions will greatly reduce the computability. In addition, for scenarios such as small power substations, photovoltaic substations and adjacent substations under the same feeder, the algorithm is limited in applicability due to objective factors such as electrical distance and photovoltaic grid-connected voltage. Summary of the invention

[0005] In view of this, the present invention provides a method, device, system and medium for verifying the relationship between household transformers in a low-voltage distribution network to solve the problems of adding additional measurement equipment and requiring high data accuracy in the prior art, thereby improving the accuracy of topology recognition technology.

[0006] A method for verifying the relationship between household transformers in a low-voltage distribution network comprises the following steps:

[0007] Extracting load events according to the load waveforms of the user and the transformer, wherein the load events include load event data in which the active power value exceeds a preset threshold;

[0008] Extracting load characteristics according to the extracted load events, wherein the load characteristics include load event occurrence time, load type, load active power value and corresponding load reactive power value;

[0009] The extracted load features are processed into characters, and arranged in sequence according to a pre-order to obtain a load event character string sequence of the user to be identified;

[0010] Calculate the longest common subsequence of the load event character string sequence of the user to be identified and the load event character string sequence of each transformer, calculate the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determine the transformer to which the user to be identified belongs according to the time deviation entropy value;

[0011] The transformer to which the determined user to be identified belongs is verified with the transformer to which the user to be identified belongs recorded in the system archives.

[0012] Furthermore, the extracted load features are characterized and arranged in sequence according to a pre-order to obtain a load event character string sequence of the user to be identified, including:

[0013] Divide the time of each day into a fixed time interval and name each interval with a number, and convert the load event occurrence time into the digital TS of the interval where the load event occurs;

[0014] Identify the load type TYPE of the corresponding load event, convert the step type character into S, convert the impact type character into P, and convert other types that are not step type characters and impact type characters into E;

[0015] The load active power value is divided into intervals of 100 watts and is processed into characters, which are recorded as QS;

[0016] The reactive power value of the load is divided into intervals of 100 var and is processed into characters, which are recorded as Q;

[0017] The four characterized characteristic characters corresponding to a single load event are combined into a character string EVENT, EVENT = {TS, TYPE, PS, QS};

[0018] Arrange the character strings corresponding to each load event at the user transformer end to be identified in the order of the load event occurrence time to obtain the load event character string sequence of the user to be identified; if the load event occurrence time TS is the same, arrange them in descending order of priority of load types S, P, and E, with S being the highest priority character; if the arrangement order is still indistinguishable, the one with a smaller PS is arranged first; if the PS is the same, the one with a smaller QS is arranged first.

[0019] Furthermore, the method of calculating the longest common subsequence of the load event character string sequence of the user to be identified and the load event character string sequence of each transformer, calculating the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determining the transformer to which the user to be identified belongs according to the time deviation entropy value, includes:

[0020] The load event character string sequence of the user to be identified is matched with the load event character string sequence of each transformer by the longest common subsequence matching calculation. The longest common subsequence refers to a sequence W, if it is a subsequence of two or more known sequences and is the longest among all sequences that meet this condition, then W is called the longest common subsequence of the known sequences;

[0021] According to the longest common subsequence obtained, backtracking is performed to extract the corresponding events in the load event sequence of the user to be identified and the load event sequence of each transformer, and the load events at the user-transformer end that constitute the elements in the subsequence are obtained to obtain a number of event pairs; each event pair is composed of an event in the user sequence and a corresponding event in the transformer sequence, forming a group of one-to-one corresponding event pairs; the character strings composed of the two load events in the event pair are exactly the same;

[0022] For each event pair, the time deviation between the user event and the transformer event is calculated in seconds. The time deviation is the absolute difference between the occurrence times of the two events.

[0023] The time deviation values ​​of all event pairs are formed into a set, and the entropy of all time deviation values ​​is calculated;

[0024] The time deviation entropy values ​​corresponding to the user and each transformer are calculated, and the transformer corresponding to the smallest time difference entropy value is determined as the transformer belonging to the user.

[0025] Furthermore, the calculation formula of the entropy of the time deviation value is: Where n is the number of different values ​​of the time deviation, p i is the probability of each deviation value appearing in the set.

[0026] A low voltage distribution network household transformer relationship verification device, comprising:

[0027] A load event extraction module, used to extract load events according to the load waveforms of users and transformers, wherein the load events include load event data whose active power value exceeds a preset threshold;

[0028] A load event extraction module, used to extract load characteristics according to the extracted load events, wherein the load characteristics include load event occurrence time, load type, load active power value and corresponding load reactive power value;

[0029] A character processing module is used to characterize the extracted load features and arrange them in a predetermined order to obtain a load event character string sequence of the user to be identified;

[0030] A user belonging identification module is used to calculate the longest common subsequence of the load event string sequence of the user to be identified and the load event string sequence of each transformer, calculate the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determine the transformer to which the user to be identified belongs according to the time deviation entropy value;

[0031] The verification module is used to verify the transformer to which the determined user to be identified belongs with the transformer to which the user to be identified belongs recorded in the system archives.

[0032] Furthermore, the character processing module is specifically used for:

[0033] Divide the time of each day into a fixed time interval and name each interval with a number, and convert the load event occurrence time into the digital TS of the interval where the load event occurs;

[0034] Identify the load type TYPE of the corresponding load event, convert the step type character into S, convert the impact type character into P, and convert other types that are not step type characters and impact type characters into E;

[0035] The load active power value is divided into intervals of 100 watts and is processed into characters, which are recorded as QS;

[0036] The reactive power value of the load is divided into intervals of 100 var and is processed into characters, which are recorded as Q;

[0037] The four characterized characteristic characters corresponding to a single load event are combined into a character string EVENT, EVENT = {TS, TYPE, PS, QS};

[0038] Arrange the character strings corresponding to each load event at the user transformer end to be identified in the order of the load event occurrence time to obtain the load event character string sequence of the user to be identified; if the load event occurrence time TS is the same, arrange them in descending order of priority of load types S, P, and E, with S being the highest priority character; if the arrangement order is still indistinguishable, the one with a smaller PS is arranged first; if the PS is the same, the one with a smaller QS is arranged first.

[0039] Furthermore, the user attribution determination module is specifically used to:

[0040] The load event character string sequence of the user to be identified is matched with the load event character string sequence of each transformer by the longest common subsequence matching calculation. The longest common subsequence refers to a sequence W, if it is a subsequence of two or more known sequences and is the longest among all sequences that meet this condition, then W is called the longest common subsequence of the known sequences;

[0041] According to the longest common subsequence obtained, backtracking is performed to extract the corresponding events in the load event sequence of the user to be identified and the load event sequence of each transformer, and the load events at the user-transformer end that constitute the elements in the subsequence are obtained to obtain a number of event pairs; each event pair is composed of an event in the user sequence and a corresponding event in the transformer sequence, forming a group of one-to-one corresponding event pairs; the character strings composed of the two load events in the event pair are exactly the same;

[0042] For each event pair, the time deviation between the user event and the transformer event is calculated in seconds. The time deviation is the absolute difference between the occurrence times of the two events.

[0043] The time deviation values ​​of all event pairs are formed into a set, and the entropy of all time deviation values ​​is calculated;

[0044] The time deviation entropy values ​​corresponding to the user and each transformer are calculated, and the transformer corresponding to the smallest time difference entropy value is determined as the transformer belonging to the user.

[0045] Furthermore, the calculation formula of the entropy of the time deviation value is: Where n is the number of different values ​​of the time deviation, p i is the probability of each deviation value appearing in the set.

[0046] A low-voltage distribution network user-transformer relationship verification system, comprising: a computer-readable storage medium and a processor;

[0047] The computer-readable storage medium is used to store executable instructions;

[0048] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the low-voltage distribution network user-transformer relationship verification method.

[0049] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for verifying the relationship between a user and a transformer in a low-voltage distribution network

[0050] The present invention has the following beneficial effects:

[0051] 1. Some existing low-voltage transformer identification methods require the addition of additional measuring equipment, resulting in high installation and operation and maintenance costs, and are prone to failure in complex scenarios, and there are certain operational safety risks. The present invention mainly utilizes the characteristics of low-voltage user electrical appliances when they are turned on. These characteristics belong to natural power consumption characteristics and do not require additional installation of equipment. In addition, these characteristics have deterministic signal properties and high recognition accuracy, which effectively solves the accuracy bottleneck of existing topology recognition technology and has great engineering application value.

[0052] 2. Some existing low-voltage transformer area household transformer identification methods use data-driven methods to identify through AMI measurement data, but the data accuracy requirements are high, and the noise in the AMI data will significantly affect its accuracy. The present invention is based on the character sequence matching method for identification, and the numerical data is processed into characters according to the interval in which it is located, which fuzzifies the numerical accuracy and reduces the requirements for data accuracy. Even in the transformer area with complex data, it can maintain a good recognition effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention is a flowchart of a method for verifying the relationship between household transformers in a low-voltage distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] See also Figure 1 The first aspect of the present invention provides a method for verifying the relationship between a low-voltage distribution network user and a transformer, comprising the following steps:

[0056] Step 1: Extract load events based on the load waveforms of users and transformers, the load events include load event data with active power values ​​exceeding a preset threshold. The load event data includes: the time period of the event, the active power and reactive power of the event, and the type of event.

[0057] Specifically, all power recording data on the same day are selected from the load waveforms of users and transformers to detect and extract load events, and the load event data with active power values ​​greater than or equal to 800 watts and their corresponding occurrence time are recorded. The time format is year-month-day-hour-minute-second, using the 24-hour timekeeping method.

[0058] Step 2: Extract load characteristics according to the load events extracted in step 1, wherein the load characteristics include load event occurrence time, load type, load active power value and corresponding load reactive power value.

[0059] According to the load event extracted in step 1, the corresponding load characteristics of the user transformer terminal (referring to the user side terminal and the transformer side terminal) to be identified are extracted and recorded. The embodiment of the present invention selects the load event occurrence time, load type, load active power value and corresponding load reactive power value to construct the load characteristics.

[0060] Step 3: Characterize the load features extracted in step 2 and arrange them in a pre-ordered manner to obtain a load event character string sequence of the user to be identified. The user to be identified refers to a set of users who need to confirm which transformer they belong to.

[0061] Step 3 specifically includes:

[0062] Step 3.1: The load event occurrence time is divided into intervals of 15 minutes and characterized, that is, a numerical range is divided into several intervals, and each value is expressed with the corresponding character of the interval.

[0063] The specific processing method is: the load event occurs at T.start, then T = seconds(T.start). The seconds(*) function is used to convert the corresponding duration into a value in seconds, such as seconds(1 minute - 25 seconds) = 85 (seconds). The characterized T is recorded as TS, TS = floor(T / 900) + 1. The floor(*) function means rounding down the input value, that is, taking the largest integer not greater than the input value, such as floor(3.2) = 3. Digital expression is a kind of character expression. For example, the character expression of 0 hours, 1 minute and 25 seconds is "1", and 1 hour, 15 minutes and 28 seconds is "6". Every day is divided into 96 time periods, each 15 minutes. The character expression of which time period this moment is in is the number of the time period. The purpose of this step is to convert the second-level moment record into minute-level, that is, minute-level moment data in a large interval every 15 minutes. In short, the number of 15-minute time periods that occur is recorded as the number. TS is the final character expression, which can be {1, 2, 3, ....95, 96}.

[0064] Step 3.2: Identify the load type TYPE of the corresponding load event, characterize the step type as S, the impulse type as P, and other types that do not belong to the above two types as E.

[0065] Step 3.3: The load active power value is divided into intervals of 100 watts and is processed as characters. The active power value calculated based on the recorded data is recorded as P, which is then recorded as PS after being converted into characters, PS=floor(P / 100)+1.

[0066] Step 3.4: The reactive power value of the load is divided into intervals of 100 var and is processed into characters. The active power value calculated according to the recorded data is recorded as Q, which is then recorded as QS after characterization, QS = floor (Q / 100) + 1.

[0067] Step 3.5: Combine the four characterized characteristic characters corresponding to a single load event into a character string EVENT, EVENT = {TS, TYPE, PS, QS}.

[0068] Step 3.6: Arrange the character strings corresponding to each load event at the user transformer end to be identified in the order of the load event occurrence time to obtain the load event character string sequence of the user to be identified; if the load event occurrence time TS is the same, arrange them in descending order of priority of load types S, P, and E, with S being the highest priority character; if the arrangement order is still indistinguishable, the one with a smaller PS is arranged first; if the PS is the same, the one with a smaller QS is arranged first.

[0069] Step 4: Calculate the longest common subsequence of the load event string sequence of the user to be identified and the load event string sequence of each transformer, calculate the time deviation entropy value corresponding to the user to be identified and each transformer based on the longest common subsequence, and determine the transformer to which the user to be identified belongs based on the time deviation entropy value.

[0070] Step 4 specifically includes:

[0071] Step 4.1: Define the longest common subsequence: For a sequence W, if it is a subsequence of two or more known sequences and is the longest among all sequences that meet this condition, then W is called the longest common subsequence of the known sequences.

[0072] Step 4.2: Perform the longest common subsequence matching calculation between the load event string sequence of the user to be identified and the load event string sequence of each transformer. Through the dynamic programming algorithm, find the longest common subsequence between the load event string sequence of the user to be identified and the load event string sequence of each transformer.

[0073] Step 4.3: According to the longest common subsequence obtained in step 4.2, backtrack and extract the corresponding events in the load event sequence of the user to be identified and the load event sequence of each transformer, and the load events at the user-transformer end that constitute the elements in the subsequence to obtain several event pairs. Each event pair consists of an event in the user sequence and a corresponding event in the transformer sequence, forming a set of one-to-one corresponding event pairs. The strings formed by the two load events in the event pair are exactly the same.

[0074] Step 4.4: For each event pair, calculate the time deviation between the user event and the transformer event in seconds. The time deviation is the absolute difference between the occurrence times of the two events, in seconds.

[0075] Step 4.5: The time deviation values ​​of all event pairs are combined into a set, and the entropy of all time deviation values ​​is calculated. The entropy calculation formula is: Where n is the number of different values ​​of the time deviation, p i is the probability of each deviation value appearing in the set.

[0076] Step 4.6: Calculate the time deviation entropy value corresponding to the user and each transformer, and determine the transformer corresponding to the smallest time difference entropy value as the transformer belonging to the user.

[0077] Step 5: Verify household change relationship

[0078] Step 5.1: Use step 4 to obtain the user change identification results of all users by using the load event sequence of each user to be identified.

[0079] Step 5.2: Compare the household transformer identification results of all users with the system files. If the transformer number of a user given by the identification result is inconsistent with the transformer number of the user recorded in the system file, the user is identified as an abnormal user.

[0080] The embodiment of the present invention selects 5 substations, and randomly selects 20 users in each substation. According to the data content and form required by the present invention, the load data of 100 users and 5 transformer terminals within 4 days are sampled. For the daily data of a certain substation, 2 users are randomly selected from each of the remaining four substations, and the data of the same day are mixed into the user data of the substation. The verification effect of the present invention is shown in Table 1:

[0081] Table 1

[0082]

[0083] According to the experimental data, this method can detect wrong users every day, with an average detection success rate of over 85%. In addition, this method only has missed detections within 4 days, and there are no wrongly detected users, so the method has high confidence.

[0084] Another aspect of the present invention provides a low-voltage distribution network household transformer relationship verification device, comprising:

[0085] A load event extraction module, used to extract load events according to the load waveforms of users and transformers, wherein the load events include load event data whose active power value exceeds a preset threshold;

[0086] A load event extraction module, used to extract load characteristics according to the extracted load events, wherein the load characteristics include load event occurrence time, load type, load active power value and corresponding load reactive power value;

[0087] A character processing module is used to characterize the extracted load features and arrange them in a predetermined order to obtain a load event character string sequence of the user to be identified;

[0088] A user belonging identification module is used to calculate the longest common subsequence of the load event string sequence of the user to be identified and the load event string sequence of each transformer, calculate the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determine the transformer to which the user to be identified belongs according to the time deviation entropy value;

[0089] The verification module is used to verify the transformer to which the determined user to be identified belongs with the transformer to which the user to be identified belongs recorded in the system archives.

[0090] Another aspect of the present invention provides a low voltage distribution network user-transformer relationship verification system, comprising: a computer readable storage medium and a processor;

[0091] The computer-readable storage medium is used to store executable instructions;

[0092] The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the low-voltage distribution network user-transformer relationship verification method described in the first aspect.

[0093] Another aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the low-voltage distribution network user-transformer relationship verification method described in the first aspect.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for verifying the relationship between household transformers in a low-voltage distribution network, characterized in that: The steps include: Extracting load events according to the load waveforms of the user and the transformer, wherein the load events include load event data in which the active power value exceeds a preset threshold; Extracting load characteristics according to the extracted load events, wherein the load characteristics include load event occurrence time, load type, load active power value and corresponding load reactive power value; The extracted load features are processed into characters, and are arranged in sequence according to a pre-order to obtain a load event character string sequence of the user to be identified; Calculate the longest common subsequence of the load event character string sequence of the user to be identified and the load event character string sequence of each transformer, calculate the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determine the transformer to which the user to be identified belongs according to the time deviation entropy value; The transformer to which the determined user to be identified belongs is verified with the transformer to which the user to be identified belongs recorded in the system archives.

2. The method for verifying the relationship between household transformers in a low-voltage distribution network according to claim 1, characterized in that: The extracted load features are characterized and arranged in sequence according to a pre-order to obtain a load event character string sequence of the user to be identified, including: Divide the time of each day into a fixed time interval and name each interval with a number, and convert the load event occurrence time into the digital TS of the interval where the load event occurs; Identify the load type TYPE of the corresponding load event, convert the step type character into S, convert the impact type character into P, and convert other types that are not step type characters and impact type characters into E; The load active power value is divided into intervals of 100 watts and is processed into characters, which are recorded as QS; The reactive power value of the load is divided into intervals of 100 var and is processed into characters, which are recorded as Q; The four characterized characteristic characters corresponding to a single load event are combined into a character string EVENT, EVENT = {TS, TYPE, PS, QS}; Arrange the character strings corresponding to each load event at the user transformer end to be identified in the order of the load event occurrence time to obtain the load event character string sequence of the user to be identified; if the load event occurrence time TS is the same, arrange them in descending order of priority of load types S, P, and E, with S being the highest priority character; if the arrangement order is still indistinguishable, the one with a smaller PS is arranged first; if the PS is the same, the one with a smaller QS is arranged first.

3. The method for verifying the relationship between household transformers in a low-voltage distribution network according to claim 1, characterized in that: The method of calculating the longest common subsequence of the load event character string sequence of the user to be identified and the load event character string sequence of each transformer, calculating the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determining the transformer to which the user to be identified belongs according to the time deviation entropy value, comprises: The load event character string sequence of the user to be identified is matched with the load event character string sequence of each transformer by the longest common subsequence matching calculation. The longest common subsequence refers to a sequence W, if it is a subsequence of two or more known sequences and is the longest among all sequences that meet this condition, then W is called the longest common subsequence of the known sequences; According to the longest common subsequence obtained, backtracking is performed to extract the corresponding events in the load event sequence of the user to be identified and the load event sequence of each transformer, and the load events at the user-transformer end that constitute the elements in the subsequence are obtained to obtain a number of event pairs; each event pair is composed of an event in the user sequence and a corresponding event in the transformer sequence, forming a group of one-to-one corresponding event pairs; the character strings composed of the two load events in the event pair are exactly the same; For each event pair, the time deviation between the user event and the transformer event is calculated in seconds. The time deviation is the absolute difference between the occurrence times of the two events. The time deviation values ​​of all event pairs are formed into a set, and the entropy of all time deviation values ​​is calculated; The time deviation entropy values ​​corresponding to the user and each transformer are calculated, and the transformer corresponding to the smallest time difference entropy value is determined as the transformer belonging to the user.

4. The method for verifying the relationship between household transformers in a low-voltage distribution network as claimed in claim 3, characterized in that: The calculation formula of the entropy of the time deviation value is: Where n is the number of different values ​​of the time deviation, p i is the probability of each deviation value appearing in the set.

5. A low voltage distribution network household transformer relationship verification device, characterized in that: include: A load event extraction module, used to extract load events according to the load waveforms of users and transformers, wherein the load events include load event data whose active power value exceeds a preset threshold; A load event extraction module, used to extract load characteristics according to the extracted load events, wherein the load characteristics include load event occurrence time, load type, load active power value and corresponding load reactive power value; A character processing module is used to characterize the extracted load features and arrange them in a predetermined order to obtain a load event character string sequence of the user to be identified; A user belonging identification module is used to calculate the longest common subsequence of the load event string sequence of the user to be identified and the load event string sequence of each transformer, calculate the time deviation entropy value corresponding to the user to be identified and each transformer according to the longest common subsequence, and determine the transformer to which the user to be identified belongs according to the time deviation entropy value; The verification module is used to verify the transformer to which the determined user to be identified belongs with the transformer to which the user to be identified belongs recorded in the system archives.

6. The low-voltage distribution network household transformer relationship verification device according to claim 5, characterized in that: The character processing module is specifically used for: Divide the time of each day into a fixed time interval and name each interval with a number, and convert the load event occurrence time into the digital TS of the interval where the load event occurs; Identify the load type TYPE of the corresponding load event, convert the step type character into S, convert the impact type character into P, and convert other types that are not step type characters and impact type characters into E; The load active power value is divided into intervals of 100 watts and is processed into characters, which are recorded as QS; The reactive power value of the load is divided into intervals of 100 var and is processed into characters, which are recorded as Q; The four characterized characteristic characters corresponding to a single load event are combined into a character string EVENT, EVENT = {TS, TYPE, PS, QS}; Arrange the character strings corresponding to each load event at the user transformer end to be identified in the order of the load event occurrence time to obtain the load event character string sequence of the user to be identified; if the load event occurrence time TS is the same, arrange them in descending order of priority of load types S, P, and E, with S being the highest priority character; if the arrangement order is still indistinguishable, the one with a smaller PS is arranged first; if the PS is the same, the one with a smaller QS is arranged first.

7. The low-voltage distribution network household transformer relationship verification device according to claim 5, characterized in that: The user attribution determination module is specifically used for: The load event character string sequence of the user to be identified is matched with the load event character string sequence of each transformer by the longest common subsequence matching calculation. The longest common subsequence refers to a sequence W, if it is a subsequence of two or more known sequences and is the longest among all sequences that meet this condition, then W is called the longest common subsequence of the known sequences; According to the longest common subsequence obtained, backtracking is performed to extract the corresponding events in the load event sequence of the user to be identified and the load event sequence of each transformer, and the load events at the user-transformer end that constitute the elements in the subsequence are obtained to obtain a number of event pairs; each event pair is composed of an event in the user sequence and a corresponding event in the transformer sequence, forming a group of one-to-one corresponding event pairs; the character strings composed of the two load events in the event pair are exactly the same; For each event pair, the time deviation between the user event and the transformer event is calculated in seconds. The time deviation is the absolute difference between the occurrence times of the two events. The time deviation values ​​of all event pairs are formed into a set, and the entropy of all time deviation values ​​is calculated; The time deviation entropy values ​​corresponding to the user and each transformer are calculated, and the transformer corresponding to the smallest time difference entropy value is determined as the transformer belonging to the user.

8. The low-voltage distribution network household transformer relationship verification device according to claim 5, characterized in that: The calculation formula of the entropy of the time deviation value is: Where n is the number of different values ​​of the time deviation, p i is the probability of each deviation value appearing in the set.

9. A low voltage distribution network household transformer relationship verification system, comprising: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the low-voltage distribution network user-transformer relationship verification method described in any one of claims 1-4.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for verifying the relationship between users and transformers in a low-voltage distribution network as claimed in any one of claims 1 to 4 is implemented.

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