Method and system for detecting line impedance anomalies

Abstract

The application provides a line impedance anomaly detection method and system. The line impedance anomaly detection method comprises: obtaining initial data of two or more users, the initial data comprising collection time, voltage sequence and current sequence; obtaining phase types of a district to which the users belong based on a K-means algorithm, wherein the Euclidean distance in the K-means algorithm is 1-p, and p is a correlation coefficient between two users; obtaining a main line and a branch line based on a distribution network line from a secondary side of a transformer to the user; calculating the main line impedance and the branch line impedance according to the initial data of the user and the phase types; obtaining an abnormal value of the main line impedance and an abnormal value of the branch line impedance based on a DBSCAN algorithm; and determining whether the abnormal value proportion of a user is greater than a limited threshold value, and if yes, determining that the user has impedance anomaly. Thus, the line of a specific user is checked and maintained to ensure the safety of the user, so as to solve the technical problem that a specific user cannot be fault identified and risk predicted based on the anomaly of the impedance parameter.

Description

Technical Field

[0001] This application relates to the field of low-voltage power distribution network technology, specifically to a method and system for detecting abnormal line impedance. Background Technology

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] As the terminal link of the power system, the stable and reliable supply of low-voltage power lines greatly affects people's production and life. Abnormal line impedance is often caused by problems such as loosening, aging, and corrosion of the lines. Therefore, line impedance parameters are effective parameters for characterizing line health and power system stability. The impedance parameters of high-voltage lines are relatively fixed compared to low-voltage lines, while the impedance parameters of low-voltage power supply lines are more complex. Therefore, traditional research has mainly focused on line impedance estimation in medium- and high-voltage transmission networks. Low-voltage distribution networks mainly rely on data collected by equipment such as SCADA (Supervisory Control and Data Acquisition) and PMU (Power Management Unit), but the adoption rate of these devices in low-voltage distribution networks is relatively low.

[0004] With the development of smart grids, power companies can wirelessly transmit electricity consumption data such as voltage and current to the central substation for storage via HPLC modules in the power grid. Based on the stored electricity consumption data, software-based methods can be considered for phase identification of users in distribution areas. For example, by collecting user voltage time series data, outlier detection algorithms can be used to verify the topological relationship between "customer-transformer" in the distribution area. However, its drawback is that it cannot clearly distinguish the phase of the distribution area to which a user belongs. Therefore, the mutual information of users in low-voltage power supply lines cannot be managed in a refined manner, and it is impossible to identify faults and predict risks for specific users based on impedance parameter anomalies. Summary of the Invention

[0005] This application provides a method and system for detecting abnormal line impedance, in order to solve the technical problem that it is impossible to identify faults and predict risks for specific users based on abnormal impedance parameters.

[0006] This application provides a method for detecting abnormal line impedance, including:

[0007] Acquire initial data from two or more users, the initial data including acquisition time, voltage sequence, and current sequence;

[0008] Based on the K-means algorithm, the phase difference of the user's station area is obtained, where the Euclidean distance in the K-means algorithm is... , The correlation coefficient between the two users;

[0009] obtaining a main line and a branch line of a power distribution network based on a line from a secondary side of a transformer to the power distribution network at a user;

[0010] calculating a main line impedance and a branch line impedance according to initial data of the user and a phase type;

[0011] obtaining an abnormal value of the main line impedance and an abnormal value of the branch line impedance based on a DBSCAN algorithm;

[0012] obtaining a user with the abnormal value;

[0013] judging whether a proportion of the abnormal value of each user is greater than a limited threshold value, and if yes, determining that the user has an impedance abnormality;

[0014] outputting a position of the user with the impedance abnormality.

[0015] Optionally, the step of calculating the main line impedance and the branch line impedance specifically comprises the steps of:

[0016] obtaining a regression equation by using a voltage drop formula according to the initial data of the user and the phase type;

[0017]

[0018] wherein, is a voltage at the transformer, is a voltage at the user, is a main line current, is a branch line current, is a main line impedance, is a branch line impedance;

[0019] calculating the main line impedance and the branch line impedance .

[0020] Optionally, after the step of calculating the main line impedance and the branch line impedance, the method further comprises the step of:

[0021] screening the main line impedance and the branch line impedance within a preset range.

[0022] Optionally, the line impedance abnormality comprises at least one of a loose terminal of an electric meter, a terminal falling off of the electric meter, a terminal rusting of the electric meter and line aging.

[0023] Optionally, after the step of obtaining the initial data of the two or more users, the method further comprises the step of:

[0024] aligning the initial data in a time axis.

[0025] Optionally, before the step of obtaining the phase of the area where the user belongs based on the K-means algorithm, the method further comprises the step of:

[0026] Based on a preset threshold, the outlier voltage value is filtered out from the voltage sequence.

[0027] The sequence segment without missing values in the filtered voltage sequence is spliced.

[0028] Optionally, the step of obtaining the initial data of two or more users comprises:

[0029] The voltage sequence and the current sequence collected by the HPLC module in the power distribution network are stored and a database is constructed.

[0030] The initial data of two or more users in a time period is obtained based on the database.

[0031] Correspondingly, the application also provides an electronic device comprising a memory and a processor, wherein the memory is used to store executable program codes; the processor is connected to the memory, and runs the computer program corresponding to the executable program codes by reading the executable program codes, so as to execute the steps in the line impedance anomaly detection method of any one of the above.

[0032] Correspondingly, the application also provides a line impedance anomaly detection system, which comprises the electronic device.

[0033] Optionally, the line impedance anomaly detection system further comprises a transformer, a power distribution network, two or more meters and an HPLC module, wherein the transformer comprises a secondary side; the meter is arranged at a branch of the power distribution network and is used to collect voltage data and current data of each user; the HPLC module is arranged at a node of a trunk and a branch of the power distribution network and is electrically connected to the electronic device; the HPLC module generates a voltage sequence according to the voltage data of at least one user at the same time and generates a current sequence according to the current data of at least one user at the same time; wherein the electronic device obtains and stores the voltage sequence and the current sequence from the HPLC module.

[0034] The application provides a line impedance anomaly detection method and system, which can quickly judge the line fault of the power distribution network and has high accuracy; at the same time, the specific user can be fault discriminated and risk predicted based on the anomaly of the impedance parameter, so that the line of the specific user can be quickly and efficiently checked and repaired, the power distribution network line can be monitored in real time, comprehensively and quickly, the safety of user power consumption can be ensured, and the line repair efficiency can be improved.

[0035] Meanwhile, the line impedance abnormality detection method can be based on the existing low-voltage distribution network, and without additional hardware devices, whether the impedance of a user is abnormal can be determined, so that the cost can be reduced, and manpower and material resources can be saved. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0037] Figure 1 is a flowchart of the line impedance abnormality detection method provided by the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. In addition, it should be understood that the specific embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In the present application, the orientation words such as "upper", "lower", "left", "right" generally refer to the upper, lower, left and right of the device in the actual use or working state, and the specific direction is the direction of the drawing in the drawings.

[0039] The present application provides a line impedance abnormality detection method and system, which will be described in detail below. It should be noted that the description order of the following embodiments is not used to limit the preferred order of the embodiments of the present application. In the following embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0040] Please refer to Figure 1 The present application provides a line impedance abnormality detection system, which is used to detect whether there is impedance abnormality in the low-voltage distribution network line in the transformer area. For the user whose impedance is detected to be abnormal, on-site inspection can be carried out to detect whether the meter terminal is loose, fallen off, rusted, and whether the line is aged, so as to improve the safety of electricity use.

[0041] The line impedance anomaly detection system mainly includes electronic equipment, transformer, power distribution network, two or more household meters, and HPLC module. The electronic equipment includes a memory and a processor. The memory is used to store executable program code. The processor is connected to the memory and runs the computer program corresponding to the executable program code by reading the executable program code to perform the steps in the line impedance anomaly detection method described below.

[0042] A transformer includes a secondary side (output end), and the electrical appliances in a user's home are connected to the transformer via the power distribution network.

[0043] The meter is installed in the user's home and electrically connected to a branch of the power distribution network to collect voltage and current data for each user. The HPLC module (High-Speed ​​Power Line Carrier, also known as High-Speed ​​Broadband Carrier) is located at the nodes between the main and branch lines of the power distribution network, and is electrically connected to the aforementioned electronic equipment.

[0044] The HPLC module mainly adopts orthogonal frequency division multiplexing (OFDM) technology, with four working frequency bands, high acquisition rate, strong anti-attenuation capability, long communication distance, minimal impact on other wireless services, and is more suitable for long-distance buried cable environments. It can effectively improve the success rate of automatic meter reading of electricity meters, realize high-frequency acquisition of voltage, current, reactive power and active power of low-voltage users, and store 96 load curves per day or hourly freeze information for 24 hours.

[0045] A household meter can collect voltage and current data in the user's home. Since the household meter is electrically connected to a branch of the power distribution network, and the smart meter based on the HPLC module is also electrically connected to the power distribution network, the HPLC module can generate a voltage sequence based on the voltage data of at least one user at the same time, and at the same time generate a current sequence based on the current data of at least one user at the same time, thereby obtaining data such as collection time, voltage sequence, and current sequence.

[0046] Smart meters based on HPLC modules can wirelessly transmit the above data to electronic devices, enabling the electronic devices to acquire and store data such as acquisition time, voltage sequence, and current sequence, and form a database.

[0047] In existing technologies, low-voltage distribution networks mainly rely on data collected by devices such as SCADA (Supervisory Control and Data Acquisition) and PMU (Power Management Unit), but these devices have a low penetration rate in low-voltage distribution networks.

[0048] The line impedance anomaly detection system can collect and transmit voltage and current data based on the HPLC module in the distribution network. Then, the processor reads the executable program code and runs the corresponding computer program to perform the line impedance anomaly detection method described below, thereby obtaining the abnormal impedance value in the line. Based on the abnormal impedance value, it can determine whether a user has an impedance anomaly, so as to assist in the fault identification and risk prediction of the distribution network and facilitate the monitoring of the user's power safety.

[0049] The line impedance anomaly detection system can be based on the existing low-voltage distribution network, without the need for additional hardware equipment, to determine whether a user has an impedance anomaly, thus reducing costs and saving manpower and resources. In existing technologies, user voltage and current data are wirelessly transmitted to electronic equipment via an HPLC module in the distribution network. A computer program then executes the line impedance anomaly detection method to obtain the abnormal line impedance value and determine whether a user has an impedance anomaly. Therefore, it can quickly identify line faults in the distribution network with high accuracy. Furthermore, based on the anomaly of impedance parameters, it can perform fault identification and risk prediction for specific users, thereby improving user electricity safety.

[0050] Please see Figure 1 This application also provides a method for detecting abnormal line impedance, which specifically includes the following steps:

[0051] S100: Stores voltage and current sequences collected by the HPLC module in the power distribution network and constructs a database;

[0052] The HPLC module can collect voltage and current data from multiple users over a period of time, thereby obtaining data such as acquisition time, voltage sequence, and current sequence. Simultaneously, a smart meter based on the HPLC module can wirelessly transmit this data to electronic devices, enabling the devices to receive and store the acquisition time, voltage sequence, and current sequence data, forming a database.

[0053] S200. Obtain initial data of two or more users within a time period based on the database. The initial data includes the acquisition time, voltage sequence, and current sequence.

[0054] Select initial data from multiple users in the database to obtain voltage and current data over a period of time. For example, select voltage and current data uploaded by the HPLC module from a certain substation from September 2021 to August 2022.

[0055] S300: Align the initial data on the timeline;

[0056] The initial data includes the acquisition time. When voltage and current data are acquired intermittently, missing dates are filled with null to facilitate subsequent algorithm calculations.

[0057] S400: Based on a preset threshold, filter out outlier voltage values ​​from the voltage sequence;

[0058] Outliers are values ​​in a dataset that differ significantly from the other values. Outliers can be identified by plotting the voltage data directly or by creating a frequency histogram of the data distribution. Alternatively, box plots of the voltage values ​​can be created using statistical software. Linear regression can also be used in statistics to identify outliers.

[0059] This application sets a preset threshold, for example, defining values ​​exceeding 8% of the voltage data average as outliers. Based on this preset threshold, outlier voltage values ​​can be filtered out from the voltage sequence, thus reflecting the data more accurately. The preset threshold is merely an example and does not represent a limitation on the scope of protection.

[0060] S500, the sequence segment without missing values ​​in the voltage sequence after splicing and screening;

[0061] Outliers that were removed differed significantly from other voltage values. Analysis was then conducted on the missing values ​​in the voltage data, and sequences without missing user values ​​were concatenated to form an initial voltage sequence. The voltage values ​​in this initial sequence showed relatively small differences and high correlation, facilitating subsequent phase identification of users based on the voltage data.

[0062] S600, based on the K-means algorithm, obtains the phase difference of users, where the Euclidean distance in the K-means algorithm is... , The correlation coefficient between the two users;

[0063] The K-means algorithm is a typical unsupervised learning algorithm, mainly used to automatically group similar users into the same category. The core idea of ​​K-means is: First, a constant K is predetermined, representing the final number of clusters. Initial points are randomly selected as centroids, and the similarity between each user and the centroid (here, Euclidean distance) is calculated to assign each user point to the most similar class. Then, the centroid of each class is recalculated (i.e., the class center), and this process is repeated until the centroids no longer change. Finally, the category to which each user belongs and the centroid of each class are determined.

[0064] The users within the same transformer substation area are classified into phases A, B, and C. Therefore, the number of clusters is set to K=3. The voltage sequences of multiple users are processed using the K-means algorithm. Based on the similarity between the user and the cluster center, the users are assigned to the most similar clusters. Then, the cluster center of each newly obtained cluster is calculated. This process is repeated until the standard measure function begins to converge. At this point, each cluster is as compact as possible, while the clusters are as far apart as possible, thus obtaining the phase of each user within the same transformer substation area.

[0065] In existing technologies, the phase information of users in distribution areas is not fully recorded in the archives. Existing distribution networks mostly rely on hardware-based on-site identification methods to classify the phase information of users in distribution areas, such as the power frequency distortion method. However, hardware-based on-site identification methods are prone to problems such as degraded power quality and high risk factors.

[0066] As power companies wirelessly transmit electricity consumption data, such as voltage and current data, to the system's main station for storage via HPLC modules in the distribution network, phase identification of users in a distribution area can be performed using software calculations. For example, by collecting user voltage time series data, outlier detection algorithms can be used to verify the topological relationship between "users and transformers" in the distribution area. However, this method cannot clearly distinguish the phase of the distribution area to which a user belongs.

[0067] The method for detecting line impedance anomalies includes a transformer substation phase identification component. First, outlier voltage values ​​are filtered out based on a preset threshold, ensuring that the filtered voltage values ​​reflect more accurate data and exhibit greater correlation due to their smaller differences. Then, the phase of each substation is determined using the K-means algorithm, where the Euclidean distance in the K-means algorithm is... , The correlation coefficient between the two users is used. Therefore, clustering is performed using the filtered voltage sequences to avoid the sensitivity to outliers and noise in the K-means algorithm, making the cluster centers less prone to shift and improving the accuracy and efficiency of user classification.

[0068] S700. Based on the distribution network lines from the secondary side of the transformer to the user, obtain the main lines and branch lines of the distribution network;

[0069] The low-voltage distribution network lines from the secondary side of the transformer to the user are simplified into two parts: "transformer-building" and "building-user". The "transformer-building" part is defined as the main line, and the "building-user" part is defined as the branch line.

[0070] The distribution network lines from the secondary side of the transformer to the user are simplified and divided into main lines and branch lines. The main lines and branch lines can be monitored separately to quickly identify parts with impedance abnormalities. This allows for more accurate identification of the locations that need to be inspected and repaired, enabling comprehensive, accurate, and efficient monitoring of power safety in the distribution network lines.

[0071] S800: Calculate the main circuit impedance and branch circuit impedance based on the user's initial data and phase.

[0072] Based on the user's acquisition time, voltage sequence, current sequence, and phase of the corresponding transformer area, the impedance of each main circuit and each branch circuit can be calculated. The specific calculation steps for the main circuit and branch circuit impedances are as follows:

[0073] S810, Based on the user's initial data and phase, the voltage at the building... It can be estimated using the voltage drop formula, i.e.

[0074]

[0075] in, The voltage at the building. The voltage at the user's location. For branch impedance, Branch current; voltage at the user's location. The branch current can be measured by the corresponding meter. It can be measured by the user's electricity meter.

[0076] Then, applying the primary voltage drop formula to the line from the transformer to the building, we obtain the regression equation:

[0077]

[0078] in, The voltage at the transformer. The voltage at the user's location. For the main circuit current, For branch current, For the main circuit impedance, Branch impedance; voltage at the transformer and the voltage at the user's location The branch current can be measured by the corresponding meter. The main circuit current can be measured by the user's electricity meter. For the current of each branch sum.

[0079] S820. Calculate the main circuit impedance and branch circuit impedance;

[0080] Based on the regression equation, a value about... In a binary linear regression problem, the voltage and current sequences from the initial data are input into a linear regression model for fitting, and an impedance value sequence can be calculated. For example, inputting voltage and current data every two hours into the linear regression model for fitting yields an impedance value. Therefore, by sequentially inputting the voltage and current data from the sequence, a set of impedance values ​​for that user can be obtained.

[0081] S830: Filter the main circuit impedance and branch circuit impedance within a preset range.

[0082] The impedance of the main circuit and the branch circuit are limited to the range of 0 to 5 ohms to screen out some main circuit and branch circuit impedances with large differences in impedance values. This results in a greater correlation between the screened main circuit impedances and the screened branch circuit impedances.

[0083] S900, based on the DBSCAN algorithm, obtains outlier values ​​of main circuit impedance and branch circuit impedance.

[0084] The DBSCAN algorithm is a density-based clustering algorithm that defines a cluster as the largest set of density-connected points. It can divide regions with sufficiently high density into clusters and can discover clusters of arbitrary shapes in noisy spatial databases.

[0085] The method for detecting line impedance anomalies includes a line impedance anomaly detection component. First, based on a preset range, the main line impedance and branch impedances are filtered to ensure that the filtered impedance values ​​more accurately reflect the data. Furthermore, the filtered main line impedances and branch impedances exhibit strong correlations. Then, the DBSCAN algorithm is used to obtain outlier values ​​in both the main line and branch impedances.

[0086] Clustering based on the filtered main and branch impedances can avoid the problem of poor clustering quality caused by uneven impedance set density and large differences in cluster spacing, thereby improving the accuracy and efficiency of impedance anomaly detection.

[0087] S1000: Retrieve users with outlier values;

[0088] Based on the abnormal values ​​of the main circuit impedance and the branch circuit impedance, users with abnormal values ​​are selected accordingly to facilitate further judgment on these users.

[0089] S1100. Determine whether the percentage of outliers for each user is greater than a certain threshold. If so, determine that there is an impedance anomaly.

[0090] When distribution network lines experience problems such as loosening, detachment, aging, or corrosion, the impedance values ​​of the lines will become abnormal. Therefore, when the proportion of abnormal impedance values ​​for a particular user exceeds a certain threshold, it can be determined that that user has an impedance anomaly during that time period. Subsequently, on-site verification is conducted for the user with the abnormality to check for problems such as loosening, detachment, corrosion, or aging of the meter terminals. This allows for rapid repair of the specific user's line, thereby ensuring the safety of the distribution network.

[0091] Since the initial data includes voltage and current sequences formed from voltage and current data over a period of time, a corresponding impedance value sequence can be obtained for each user. Then, based on the DBSCAN algorithm, impedance anomalies in this sequence can be filtered out, and the proportion of the filtered impedance anomalies in the impedance value sequence is calculated. When the proportion of anomalies for a user exceeds a predetermined threshold, it can be determined that the corresponding line in the user's transformer area has an impedance anomaly.

[0092] In this application, if the proportion of an impedance anomaly value of a certain user exceeds 5%, it can be determined that the user has an impedance anomaly during this period. The above-mentioned threshold is only an example and does not constitute a limitation on the scope of protection.

[0093] S1200, location of users with abnormal output impedance;

[0094] The system sequentially determines whether each user has an impedance abnormality, and then outputs the location of the user with an impedance abnormality through electronic equipment or other components, so that the power company can carry out emergency repairs on the lines of the aforementioned users and ensure the users' power safety.

[0095] The line impedance anomaly detection method in this application includes a transformer substation user phase identification method and a user impedance anomaly detection method. Based on the user's voltage sequence, current sequence, and acquisition time, the phase of each user's transformer substation can be determined using the K-means algorithm. Simultaneously, the distribution network lines from the transformer secondary side to the user are simplified into main lines and branch lines. Therefore, a specific user can be identified based on a main line and a branch line connected electrically.

[0096] Based on the aforementioned initial data and the phase of the distribution area, the main line impedance and branch line impedance can be calculated. Then, based on the DBSCAN algorithm, the abnormal values ​​of the main line impedance and branch line impedance can be obtained. Based on the impedance abnormal values, the specific user with impedance abnormality can be identified. Therefore, the main line and corresponding branch of the abnormal user can be identified from the distribution network line.

[0097] The method for detecting abnormal line impedance can quickly identify line faults in the distribution network with high accuracy. At the same time, it can identify faults and predict risks for specific users based on the abnormal impedance parameters. Therefore, it can quickly and efficiently inspect and repair the lines of specific users, realize real-time, comprehensive and rapid monitoring of distribution network lines, ensure the safety of users' electricity use, and improve the efficiency of line emergency repair.

[0098] Meanwhile, the method for detecting abnormal line impedance can be based on the existing low-voltage distribution network. It can determine whether a user has an impedance abnormality without the need to install additional hardware equipment, thus reducing costs and saving manpower and resources.

[0099] The above provides a detailed description of the method and system for detecting abnormal line impedance. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting abnormal line impedance, characterized in that, include: Acquire initial data from two or more users, the initial data including acquisition time, voltage sequence, and current sequence; Based on a preset threshold, outlier voltage values ​​are filtered out from the voltage sequence. The sequence segments without missing values ​​in the voltage sequence after splicing and filtering are then analyzed. Based on the K-means algorithm, the phase of the user's station area is obtained, where the Euclidean distance in the K-means algorithm is 1-ρ, and ρ is the correlation coefficient between the two users; Based on the distribution network lines from the secondary side of the transformer to the user, obtain the main lines and branch lines of the distribution network; Calculate the main circuit impedance and branch circuit impedance based on the user's initial data and phase. The steps for calculating the main circuit impedance and branch circuit impedance specifically include the following steps: Based on the user's initial data and phase, the regression equation is obtained using the voltage drop formula: U0-U2=Z1I1+Z2I2 Where U0 is the voltage at the transformer, U2 is the voltage at the user, I1 is the main circuit current, I2 is the branch circuit current, Z1 is the main circuit impedance, and Z2 is the branch circuit impedance. Calculate the main circuit impedance Z1 and the branch circuit impedance Z2; Based on the DBSCAN algorithm, outlier values ​​of main circuit impedance and branch circuit impedance are obtained. Retrieve users whose values ​​are out of the ordinary; Determine whether the percentage of outliers for each user is greater than a certain threshold. If so, determine that the user has an impedance anomaly. Location of users with abnormal output impedance.

2. The method for detecting abnormal line impedance according to claim 1, characterized in that, Following the steps of calculating the main circuit impedance and branch circuit impedance, the method further includes the following steps: Select the main circuit impedance and branch circuit impedance within a preset range.

3. The method for detecting abnormal line impedance according to claim 1, characterized in that, The abnormal line impedance includes at least one of the following: loose meter terminals, detached meter terminals, corroded meter terminals, and aging lines.

4. The method for detecting abnormal line impedance according to claim 1, characterized in that, After the step of obtaining initial data from two or more users, the method further includes the following steps: Align the initial data on the timeline.

5. The method for detecting abnormal line impedance according to claim 1, characterized in that, The steps for obtaining initial data from two or more users include: Store the voltage and current sequences collected by the HPLC module in the power distribution network and build a database; Retrieve initial data for two or more users within a given time period from a database.

6. An electronic device, characterized in that, include: Memory, used to store executable program code; as well as A processor, connected to the memory, runs a computer program corresponding to the executable program code by reading the executable program code to perform the steps in the method for detecting line impedance anomalies as described in any one of claims 1-5.

7. A system for detecting abnormal line impedance, characterized in that, Including the electronic device as described in claim 6.

8. The line impedance anomaly detection system according to claim 7, characterized in that, Also includes: A transformer, which includes a secondary side; The distribution network, through which electrical equipment is electrically connected to the transformer; two or more household meters, which are installed on branches of the distribution network, to collect voltage and current data for each user; as well as An HPLC module is located at the node between the main and branch lines of the power distribution network and is electrically connected to the electronic equipment. The HPLC module generates a voltage sequence based on voltage data from at least one user at the same time, and generates a current sequence based on current data from at least one user at the same time. The electronic device acquires and stores the voltage sequence and the current sequence from the HPLC module.

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