Wiring error leakage user positioning method and system based on elastic network regression, terminal and medium

Abstract

The application discloses a kind of based on elastic network regression's wiring error leakage user positioning method, system, terminal and medium, wherein method includes: obtaining the abnormal station area leakage fault day's station area residual current data, user load current data, and constructs station area residual current time series and subordinate user load current time series;With the user load current data obtained as explanatory variable, station area residual current data as explained variable, carry out elastic network regression calculation, obtain the optimal explanatory variable after eliminating multicollinearity and its corresponding regression coefficient and construct regression model;Compare the absolute value of each regression coefficient, and the user whose absolute value of regression coefficient is greater than preset threshold is judged as zero line, ground line wiring error user.Through identifying zero line, ground line wiring error abnormal user, to solve the existing low-voltage station area exists because user zero line, ground line wiring error and lead to user load current data into residual current problem.

Description

Technical Field

[0001] This invention relates to the field of leakage current detection and analysis in low-voltage distribution networks, and in particular to a method, system, terminal, and medium for locating users with leakage current due to wiring errors based on elastic network regression. Background Technology

[0002] Low-voltage distribution networks are the final stage of the power system, directly connected to users, and bear the crucial responsibility of providing them with safe, reliable, and high-quality electricity. However, due to their large scale, complex lines, aging equipment, and insufficient maintenance, low-voltage distribution networks are prone to leakage current. This not only affects the efficiency of electricity utilization but also endangers the lives and property of users and the stable operation of the power system. Therefore, leakage current detection and analysis in low-voltage distribution networks has significant theoretical and practical value.

[0003] HPLC smart meters have been widely used and promoted in low-voltage power distribution networks in recent years. Compared with traditional meters, HPLC smart meters can report metering data at 15-minute intervals, and have advantages such as stable communication channels, strong real-time performance, high acquisition rate, and large information capacity. They can effectively transmit power parameters such as active power and reactive power to the terminal through high-speed broadband power line carrier technology, providing reliable data support for power system monitoring and control. At the same time, the wide coverage of the HPLC module can ensure the quality and accuracy of data acquisition, reducing the cost and difficulty of operation and maintenance.

[0004] A residual current device (RCD) is a device that senses and measures the residual current in a circuit. When the residual current exceeds a set value, it automatically disconnects the power supply, thus achieving a protective function. With the transformation and upgrading of power grids, many regions adopt a three-level RCD system (main RCD for distribution transformers, branch line RCD, and terminal household RCD) to ensure the reliability and safety of power supply. The three-level RCD system achieves graded protection by setting different threshold values ​​for the residual current of each level of RCD and different delay times between each level.

[0005] Under normal circumstances, due to factors such as aging lines and damaged insulation coatings, a certain degree of residual current exists in low-voltage distribution areas. To ensure the proper functioning of branch line leakage protection and terminal household leakage protection, the residual current setting value of the main leakage protection device on the low-voltage side of the distribution transformer outlet is generally 300mA. When the residual current exceeds this threshold, the main leakage protection device will trip, thus providing protection. However, in actual operation, if the main leakage protection device trips frequently, the users in the low-voltage distribution area will be unable to use electricity normally, seriously affecting their daily lives. To solve this problem, it is often necessary to take the main leakage protection device out of service to ensure the continuity of power supply. However, doing so increases the risk of electric shock and fire for users, posing a significant threat to their personal and property safety.

[0006] When a user reverses the neutral and ground wires at the incoming line, the user's load current will no longer flow back through the neutral wire, but instead flow into the ground through the ground wire, becoming entirely residual current. Since the load current usually far exceeds the 300mA limit, when the main residual current circuit breaker of the distribution transformer detects that the residual current exceeds the set value, it will trip and cut off the power supply. In this situation, the residual current in the low-voltage distribution area mainly consists of the load current of the user with the incorrect wiring, while the residual current of other users is relatively small.

[0007] Therefore, there is an urgent need for a method to locate and detect users with leakage faults when abnormal situations such as incorrect connection of neutral or ground wire occur in low-voltage distribution areas. Summary of the Invention

[0008] This invention provides a method, system, terminal, and medium for locating users with wiring errors and leakage current based on elastic network regression. The method identifies abnormal users with incorrect neutral and ground wire wiring, thereby solving the problem in existing low-voltage distribution areas where user load current data is converted into residual current due to user neutral and ground wire wiring errors.

[0009] In a first aspect, the present invention provides a method for locating users with wiring errors and leakage current based on elastic network regression, comprising:

[0010] The system acquires residual current data and user load current data for the day the leakage fault occurred in the transformer substation, and constructs a time series of residual current for the substation and a time series of load current for its subordinate users. Specifically, the data is acquired using a new type of HPLC smart meter and transmitted to the backend system of the electricity information collection system or the smart terminal in the transformer substation.

[0011] By using user load current data as the explanatory variable and transformer substation residual current data as the explained variable, elastic network regression calculations are performed to obtain the optimal explanatory variable and its corresponding regression coefficients after eliminating multicollinearity, and a regression model is constructed. Using elastic network regression calculations, correlated explanatory variables can be screened and reduced, thereby eliminating some explanatory variables with multicollinearity, making the subsequently constructed model more fully reflect the relationship between transformer substation residual current and user load current.

[0012] Compare the absolute values ​​of each regression coefficient, and determine users whose absolute values ​​of regression coefficients are greater than a preset threshold as users with incorrect neutral or ground wire connections.

[0013] The method described in this invention identifies abnormal distribution areas where residual current exceeds a threshold, causing frequent tripping of the residual current protection device (RCD) in the low-voltage distribution area. It then acquires the residual current data of the distribution area and the load current data of the users on the day the leakage fault occurred. Elastic network regression analysis is used to process and analyze the data, filtering and reducing correlated explanatory variables, eliminating collinearity, and obtaining a regression model of the distribution area's residual current with respect to the load current of each user. This allows for the identification of abnormal users with incorrect neutral and ground wire connections. This method provides an efficient and accurate detection scheme for locating users with leakage faults due to wiring errors in low-voltage distribution areas, and provides an effective technical means to improve the reliability and continuity of power grid supply and ensure user electricity safety.

[0014] Furthermore, the abnormal distribution area is defined as a distribution area where the residual current exceeds the preset threshold of the residual current device (RCD), causing frequent tripping of the low-voltage RCD. In specific implementation, on the low-voltage side of the distribution transformer outlet, the residual current setting value of the main RCD is generally 300mA. When the residual current value of the distribution area exceeds this threshold, the main RCD will trip, thus the distribution area that trips frequently will be identified as an abnormal distribution area.

[0015] Furthermore, the loss function of the elastic network regression is:

[0016]

[0017] in, It is the target variable. It is the independent variable. These are the parameters of the elastic network regression model. Describing the L1 norm, Represents the L2 norm. It is a hyperparameter of regularization strength. This refers to the weight ratio of the L1 and L2 regularization terms. The weight ratio of the regularization terms is adjusted accordingly. This allows for a balance between L1 and L2 regularization, effectively controlling the number and size of model parameters while ensuring model stability and improving the model's generalization ability.

[0018] Furthermore, the elastic network regression specifically refers to:

[0019] when When the elastic network regression is at its minimum, it fits the least squares linear regression model; when... , When the elastic network regression is the model fitted by lasso regression, then... , When the elastic network regression is the ridge regression fitting model; when , In this context, elastic network regression is a combination of ridge regression and lasso regression. This allows elastic network regression to achieve the purpose of variable selection, reducing the coefficients of irrelevant or unimportant features to zero, while also exhibiting a good grouping effect, that is, selecting or excluding related feature groups when they exist in the data.

[0020] Secondly, the present invention provides a wiring error leakage current user location system based on elastic network regression, comprising:

[0021] Data acquisition module: used to acquire the residual current data of the transformer area and the user load current data on the day the leakage fault occurred in the abnormal transformer area;

[0022] The regression model construction module is used to perform elastic network regression calculations using user load current data as explanatory variables and transformer area residual current data as explained variables, to obtain the optimal explanatory variables and their corresponding regression coefficients after eliminating multicollinearity, and to construct a regression model.

[0023] Leakage fault user location module: compares the absolute values ​​of each regression coefficient, and determines users whose absolute values ​​of regression coefficients are greater than preset thresholds as users with incorrect neutral or ground wire connections.

[0024] Thirdly, the present invention provides a terminal comprising one or more processors and a memory storing one or more programs, wherein the processors call the programs in the memory to implement the steps of a method for locating leakage current in user neutral and ground wire wiring errors based on resilient network regression.

[0025] Fourthly, the present invention provides a storage medium storing a computer program that is invoked by a processor to implement the steps of a method for locating leakage current in user neutral and ground wire wiring errors based on resilient network regression.

[0026] Beneficial effects

[0027] This invention proposes a method, system, terminal, and medium for locating users with wiring errors and leakage current based on elastic network regression. The method identifies abnormal distribution areas where residual current exceeds a threshold, causing frequent tripping of the residual current protection device (RCD) in the low-voltage distribution area. It then acquires the residual current data of the distribution area and the load current data of the users on the day the leakage fault occurred. Elastic network regression analysis is used to process and analyze the data, filtering and reducing correlated explanatory variables, eliminating collinearity, and obtaining a regression model of the distribution area's residual current with respect to the load current of each user. This allows for the identification of abnormal users with wiring errors in the neutral and ground wires. This method provides an efficient and accurate detection scheme for locating users with wiring errors and leakage current in low-voltage distribution areas, offering an effective technical means to improve the reliability and continuity of power grid supply and ensure user electricity safety. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of a wiring error leakage current location method based on elastic network regression provided in an embodiment of the present invention;

[0030] Figure 2 This is the residual current curve of the low-voltage distribution area when the leakage current protector does not operate, as provided in this embodiment of the invention.

[0031] Figure 3 These are the residual current and abnormal user load current curves for the transformer area provided in this embodiment of the invention;

[0032] Figure 4 These are the residual current and abnormal user load current curves for the transformer area provided in this embodiment of the invention;

[0033] Figure 5 This is the selection curve for the explanatory variables of the transformer area in the elastic network regression provided in the embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0035] Example 1

[0036] like Figure 1 As shown, this embodiment provides a method for locating leakage current in user neutral and ground wire wiring errors based on elastic network regression, including:

[0037] S1: Obtain the residual current data of the transformer area and the user load current data on the day the leakage fault occurred in the abnormal transformer area, and construct the time series of the residual current of the transformer area and the time series of the load current of the subordinate users.

[0038] Specifically, the abnormal distribution area is defined as a distribution area where the residual current exceeds the preset threshold of the residual current device (RCD), causing frequent tripping of the low-voltage RCD. In practice, on the low-voltage side of the distribution transformer outlet, the residual current setting of the main RCD is typically 300mA. When the residual current value of the distribution area exceeds this threshold, the main RCD will trip, thus identifying the frequently tripping distribution area as an abnormal area. The residual current data of the distribution area and its users are collected by a new type of HPLC smart meter and sent to the backend system of the electricity information collection system or the smart terminal of the distribution area. In this embodiment, the required residual current data of the distribution area and user load current data are extracted from the smart terminal of the distribution area. Based on the extracted data, a time series of residual current in the transformer substation and a time series of load current of subordinate users are constructed. The sampling interval t for constructing the time series of residual current in the transformer substation and the time series of load current of users ranges from 1 to 30 minutes. The HPLC smart meter can transmit and report data once every 15 minutes. Based on the current popularity of HPLC smart meters, this embodiment selects a sampling interval of 15 minutes, that is, 96 sampling points per day.

[0039] S2: Using user load current data as the explanatory variable and transformer substation residual current data as the explained variable, elastic network regression is performed to obtain the optimal explanatory variable and its corresponding regression coefficients after eliminating multicollinearity, and a regression model is constructed. Using elastic network regression, correlated explanatory variables can be screened and reduced, thereby eliminating some explanatory variables with multicollinearity, making the subsequently constructed model more fully reflect the relationship between transformer substation residual current and user load current.

[0040] Specifically, in this embodiment, the elastic regression network calculation is based on an elastic network regression algorithm model built using Python. It establishes a correlation between the residual current time series of the transformer area (target variable) and the load current time series of subordinate users (independent variables) to obtain the optimal explanatory variables and their corresponding regression coefficients after eliminating multicollinearity. The loss function of the elastic network regression is:

[0041]

[0042] in, The target variable is the time series of the residual current in the transformer substation, which is used in this embodiment. The independent variable is the time series of the load current of the subordinate users in this embodiment. These are the parameters of the elastic network regression model. Describing the L1 norm, Describing the L2 norm, It is a hyperparameter of regularization strength. This refers to the weight ratio of the L1 and L2 regularization terms. The weight ratio of the regularization terms is adjusted accordingly. This can achieve a balance between L1 and L2 regularization, thereby effectively controlling the number and size of model parameters and improving the model's generalization ability while ensuring model stability.

[0043] More specifically, the elastic network regression is as follows:

[0044] when When the elastic network regression is at its minimum, it fits the least squares linear regression model; when... , When the elastic network regression is the model fitted by lasso regression, then... , When the elastic network regression is the ridge regression fitting model; when , In this context, elastic network regression is a combination of ridge regression and lasso regression. This allows elastic network regression to achieve the purpose of variable selection, reducing the coefficients of irrelevant or unimportant features to zero, while also exhibiting a good grouping effect, that is, selecting or excluding related feature groups when they exist in the data.

[0045] Based on the obtained optimal explanatory variables and their regression coefficients, the elastic network regression equation for the corresponding explained variable is derived, namely, the elastic network regression equation of the residual current in the transformer area with respect to the user load current.

[0046]

[0047] The above equation is a p-variable linear regression equation, where y is the explanatory variable; y is the explained variable; is the regression coefficient.

[0048] S3: Compare the absolute values ​​of each regression coefficient. Users whose absolute values ​​of regression coefficients are greater than a preset threshold are identified as having incorrect neutral or ground wire connections. The preset threshold can be set according to actual conditions and is not limited. In this embodiment, the preset threshold is set such that if a certain coefficient is one order of magnitude larger than other coefficients when comparing coefficients, then this coefficient is considered to be much larger than the other coefficients.

[0049] The method described in this invention identifies abnormal distribution areas where residual current exceeds a threshold, causing frequent tripping of the residual current protection device (RCD) in the low-voltage distribution area. It then acquires the residual current data of the distribution area and the load current data of the users on the day the leakage fault occurred. Elastic network regression analysis is used to process and analyze the data, filtering and reducing correlated explanatory variables, eliminating collinearity, and obtaining a regression model of the distribution area's residual current with respect to the load current of each user. This allows for the identification of abnormal users with incorrect neutral and ground wire connections. This method provides an efficient and accurate detection scheme for locating users with leakage faults due to wiring errors in low-voltage distribution areas, and provides an effective technical means to improve the reliability and continuity of power grid supply and ensure user electricity safety.

[0050] Example 2

[0051] This embodiment provides a wiring error leakage current user location system based on elastic network regression, including:

[0052] Data acquisition module: used to acquire the residual current data of the transformer area and the user load current data on the day the leakage fault occurred in the abnormal transformer area, and to construct the time series of the residual current of the transformer area and the time series of the load current of the subordinate users;

[0053] The regression model construction module is used to perform elastic network regression calculations using user load current data as explanatory variables and transformer area residual current data as explained variables, to obtain the optimal explanatory variables and their corresponding regression coefficients after eliminating multicollinearity, and to construct a regression model.

[0054] Leakage fault user location module: compares the absolute values ​​of each regression coefficient, and determines users whose absolute values ​​of regression coefficients are greater than preset thresholds as users with incorrect neutral or ground wire connections.

[0055] Example 3

[0056] The present invention provides a terminal comprising one or more processors and a memory storing one or more programs, wherein the processors call the programs in the memory to implement the steps of a method for locating leakage current in user neutral and ground wire wiring errors based on resilient network regression.

[0057] Example 4

[0058] The present invention provides a storage medium storing a computer program, which is called by a processor to implement the steps of a method for locating leakage current in user neutral and ground wire wiring errors based on elastic network regression.

[0059] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0060] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the controller described in any of the foregoing embodiments, such as the controller's hard drive or memory. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the readable storage medium can include both the controller's internal storage unit and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0061] Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The technical solution provided by the present invention will be further explained below with reference to a specific example.

[0063] Taking the daily load current data and residual current data of 240 users under a low-voltage distribution substation as an example, this paper specifically illustrates the process and effect of the method of the present invention in detecting users with incorrect neutral and ground wire connections. All connected users under the substation have been equipped with new HPLC smart meters. The current residual current in the substation is as follows: Figure 2 As shown, the horizontal axis represents the sampling time points, with a sampling interval of 15 minutes, resulting in a total of 96 sampling points per day. The vertical axis represents the residual current value of the transformer area. Figure 2 As can be seen, the residual current value in the normal distribution area is less than 300mA, and the main leakage current protection device of the distribution transformer does not trip.

[0064] The load current data of 96 points per day for the 240 users under the jurisdiction of the transformer area are defined as a sequence. The residual current data of the transformer area is defined as a sequence 𝑌, and the residual current sequence of the leaking transformer area and the load current sequence of each connected user are constructed.

[0065] User 197 encountered a wiring error in the neutral and ground wires at the incoming end, such as... Figure 3 As shown, at this time, the load current of user 197 is entirely converted into the residual current of the transformer area, i.e. The residual current sequence of the transformer area and the load current sequence of each connected user are imported into the elastic network regression algorithm fitting model built based on Python, and elastic network regression calculation is performed to construct the elastic network regression model. The absolute values ​​of the regression coefficients of each user are compared, and users whose absolute values ​​of regression coefficients are much larger than others are judged as users whose leakage is caused by incorrect neutral or ground wire wiring.

[0066] The regression coefficients for each user, calculated using elastic network regression, are shown in Table 1.

[0067]

[0068]

[0069] By calculating and organizing the explanatory variables using elastic network regression, we can obtain the elastic network regression equation:

[0070]

[0071] The regression equation shows that the coefficient of explanatory variable 197 is 0.89854. Comparing this coefficient with other explanatory variables reveals that the coefficient of 197 is significantly larger. Therefore, user 197 in the leaky electrical area is identified as a suspected user with leakage due to incorrect neutral and ground wire wiring. On-site inspection verified the analysis results as correct.

[0072] In addition, select another transformer area, and the residual current and abnormal user load current of the transformer area are as follows: Figure 4As shown, the load current data of 96 points from 240 users under the jurisdiction of the transformer area during a day are defined as a sequence. The residual current data of the transformer area is defined as a sequence 𝑌, and the residual current sequence of the leaking transformer area and the load current sequence of each connected user are constructed.

[0073] Users 116 and 133 have incorrect neutral and ground wire connections at their incoming terminals, and their load currents are out of phase. At this time, the load currents of both users 116 and 133 are converted into residual currents in the transformer area. Let the load current of user 116 be... The load current of user 133 is The residual current in the low-voltage distribution area is approximately equal to the sum of the load currents of user 116 and user 133 (out-of-phase). The residual current sequence of the transformer area and the load current sequence of each connected user are imported, and elastic network regression calculations are performed to construct an elastic network regression model. The selection process curve for the explanatory variables of the elastic network regression is shown in the figure. Figure 5 As shown, by comparing the absolute values ​​of the regression coefficients of each user, users whose absolute values ​​of regression coefficients are much larger than those of other users are identified as users whose leakage is caused by incorrect wiring of the neutral or ground wire.

[0074] The regression coefficients for each user are shown in Table 2.

[0075]

[0076]

[0077] By calculating and organizing the explanatory variables using elastic network regression, we can obtain the elastic network regression equation:

[0078]

[0079] The regression equation shows that the coefficient of explanatory variable 116 is 0.86795, and the coefficient of explanatory variable 133 is 0.82501. Comparison with the coefficients of other explanatory variables reveals that the coefficients of explanatory variables 116 and 133 are significantly larger than those of the others. Therefore, users 116 and 133 in the leaky transformer area are identified as suspected users with leakage due to incorrect neutral and ground wire wiring. On-site inspection verified the analysis results as correct.

[0080] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0081] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for locating users with wiring errors and leakage current based on elastic network regression, characterized in that, include: Acquire residual current data and user load current data of the abnormal distribution area on the day the leakage fault occurs, and construct the time series of residual current of the distribution area and the time series of load current of subordinate users; wherein, the abnormal distribution area is the distribution area where the residual current exceeds the preset threshold of the leakage current protection device and causes the low-voltage distribution area leakage current protection device to trip frequently. Using user load current data as explanatory variables and transformer area residual current data as explained variables, elastic network regression calculations are performed to obtain the optimal explanatory variables and their corresponding regression coefficients after eliminating multicollinearity, and a regression model is constructed. Compare the absolute values ​​of each regression coefficient, and determine users whose absolute values ​​of regression coefficients are greater than a preset threshold as users with incorrect neutral or ground wire connections.

2. The method for locating users with wiring errors and leakage current based on elastic network regression according to claim 1, characterized in that, The loss function for the elastic network regression is: ； in, It is the target variable. It is the independent variable. These are the parameters of the model. Describing the L1 norm, Represents the L2 norm. It is a hyperparameter of regularization strength. It represents the weight ratio of L1 and L2 regularization terms.

3. The method for locating users with wiring errors and leakage current based on elastic network regression according to claim 2, characterized in that, The elastic network regression specifically refers to: when At that time, the elastic network regression is a least squares linear regression fitting model; when , At that time, the elastic network regression is a model fitted by lasso regression; when , At that time, the elastic network regression model is a ridge regression fit model; when , In this case, elastic network regression is a combination of ridge regression and lasso regression.

4. A wiring error leakage current user location system based on elastic network regression, characterized in that, include: Data acquisition module: used to acquire residual current data of the transformer area and user load current data on the day the leakage fault occurs in the abnormal transformer area, and to construct the time series of residual current of the transformer area and the time series of load current of subordinate users; wherein, the abnormal transformer area is the transformer area where the residual current exceeds the preset threshold of the leakage current protection device and causes the low-voltage transformer area leakage current protection device to trip frequently. The regression model construction module is used to perform elastic network regression calculations using user load current data as explanatory variables and transformer area residual current data as explained variables, to obtain the optimal explanatory variables and their corresponding regression coefficients after eliminating multicollinearity, and to construct a regression model. Leakage fault user location module: compares the absolute values ​​of each regression coefficient, and determines users whose absolute values ​​of regression coefficients are greater than preset thresholds as users with incorrect neutral or ground wire connections.

5. A terminal, characterized in that: It includes one or more processors and a memory storing one or more programs, wherein the processors invoke the programs in the memory to implement the steps of the method according to any one of claims 1-3.

6. A readable storage medium, characterized in that: A computer program is stored, which is invoked by a processor to implement the steps of the method according to any one of claims 1-3.

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