Meter connector oxidation quantification identification method based on voltage instantaneous drop data

Abstract

The invention discloses a meter connector oxidation quantification identification method based on voltage instantaneous drop data. The method comprises the steps that S1, related information is acquired through a data center; s2, performing data processing on the related information so as to define a low-voltage user and a voltage instantaneous drop event, and performing data cleaning on the voltage instantaneous drop event so as to remove related instantaneous drop data; and S3, setting each risk dimension so as to calculate the risk value of the meter connector. According to the meter connector oxidation quantification identification method based on the voltage instantaneous drop data, the voltage instantaneous drop data characteristics of low-voltage users are analyzed, the oxidation and loosening degree of the meter connector and the critical degree of the meter connector are quantified by combining the user measurement data of the same transformer area, the transformer area gateway general meter measurement data and the transformer area topology, and the meter connector oxidation quantification identification method based on the voltage instantaneous drop data is obtained. And equipment owners are informed through the system and short messages, so that maintainers can conveniently find the problems in advance.

Description

Technical Field

[0001] The invention belongs to the technical field of electric power meter identification, and in particular relates to a method for identifying oxidation of a meter joint based on voltage sag and a hierarchy analysis algorithm. Background Art

[0002] The front connector of the electricity meter is generally a copper connector. There are often multiple connectors and terminal blocks for the neutral and live wires in the meter box. During operation, oxidation and loosening of the connectors are inevitable. As the corresponding connector impedance increases due to oxidation and loosening, users often experience a rapid drop in voltage after using electricity, leading to frequent power outages and unusable electrical appliances. This seriously affects normal electricity use. Long-term use can easily cause connector burns or even fires, affecting safety and production.

[0003] The existing solution of power supply companies generally uses on-site inspections to check the status of meter connectors. Since the connectors are invisible inside the meter terminals and are hidden, oxidation and loosening are difficult to detect in time. They are often not discovered until users complain or the meter burns out.

[0004] Therefore, further improvements are made to the above problems. Summary of the invention

[0005] The main purpose of the present invention is to provide a quantitative identification method for meter joint oxidation based on voltage sag data, analyze the voltage sag data characteristics of low-voltage users, combine the measurement data of users in the same area, the measurement data of the total meter at the area gateway and the area topology, quantify the degree of oxidation and loosening of the meter joints, quantify the criticality of the meter joints, and inform the equipment owner through the system and text messages, so as to facilitate maintenance personnel to discover such problems in advance, eliminate faults in a targeted and preventive manner, and avoid frequent power outages and voltage instability affecting users' electricity consumption and joint burning.

[0006] To achieve the above objectives, the present invention discloses a method for quantitatively identifying oxidation of meter joints based on voltage drop data, comprising the following steps:

[0007] Step S1: Obtain relevant information through the data center;

[0008] Step S2: Processing the relevant information to define low voltage users and voltage sag events, and cleaning the voltage sag events to remove relevant sag data;

[0009] Step S3: Set various risk dimensions to calculate the risk value of the meter connector. The input data is the voltage drop data of the user within a preset time (within 1 month), and the output is a quantitative risk value.

[0010] As a further preferred technical solution of the above technical solution, step S1 is specifically implemented as follows:

[0011] Obtaining first information including household number, household name, power supply station, address, power supply voltage, contact information, operating capacity, corresponding relationship between household number and meter, meter wiring method, meter maintainer information, and corresponding relationship between user and meter box through the user information table and the meter equipment information table;

[0012] Through the user's power load data table, obtain the second information including the A-phase, B-phase, and C-phase currents, A-phase, B-phase, and C-phase voltages, and the A-phase, B-phase, and C-phase apparent powers of the user's meter measurement points (96 measurement points in one day) (the measurement point data is an instantaneous value, the three-phase meter takes the three phases, and the single-phase meter takes the overlapped phases);

[0013] Obtain the corresponding relationship information of the user belonging to the substation through the electricity connection information table;

[0014] Through the user power outage information table, obtain the power outage and power restoration time information within the user's preset time (1 month).

[0015] As a further preferred technical solution of the above technical solution, step S2 is specifically implemented as follows:

[0016] First, traverse the user voltage data. When the voltage of one phase of the user is less than the threshold (215V), and at the same time, the voltage difference between the corresponding phase of the main meter of the area and the corresponding phase of the user is greater than the preset difference (10V), it is defined as a low-voltage user caused by the voltage difference. The user account number, voltage and current data of the day of occurrence are retained, updated daily, and historical data of the preset time (1 month) is retained;

[0017] When a certain phase of the user has low voltage, obtain the voltage at the low voltage moment and the maximum voltage of a (preferably 3) measurement points before the moment. If the voltage difference between the maximum voltage of the a measurement points before the low voltage moment and the voltage at the low voltage moment exceeds a preset difference (10V), it is defined as a voltage sag event for the user, and the start and end times of the voltage sag event are recorded, as well as the voltage and current at the two times, and the voltage difference and current difference at the start and end of the voltage sag event are saved;

[0018] Power outage data cleaning: If the user's voltage drops to near 0, and the current drops to near 0 at the same time, and the maximum current at the non-voltage drop time of the day is greater than the current threshold (user operating capacity / 220*0.2), the voltage drop is defined as caused by power outage, the user is marked as outage and the data of this drop is removed;

[0019] Phase loss data cleaning: If the low voltage value of the user's voltage drop and the voltage value of the corresponding phase of the substation main meter at that moment are both less than the voltage threshold (180V), it is defined as a phase loss causing a drop and the drop data is removed;

[0020] Multiple users with the same voltage drop cleaning: If multiple users have voltage drops at the same time under the same transformer (station area), it is defined as caused by branch lines or multi-household long power supply radius, and this batch of voltage drop data is eliminated;

[0021] Large current data cleaning: If the voltage of a user drops to a low voltage and the current is greater than the preset current (30A) during a transient voltage drop event, it is defined as a large current transient voltage drop and the transient voltage drop data is removed;

[0022] Instantaneous voltage recovery cleaning: record the current when the voltage drops to low voltage in a certain instantaneous voltage event of the user. If the voltage returns to normal within b measurement points after the instantaneous voltage drops and the current at the normal voltage measurement point is greater than the current at the low voltage moment * threshold, it is defined as a instantaneous voltage drop not caused by connector oxidation and the instantaneous voltage drop data is discarded.

[0023] Transformer synchronous instantaneous drop cleaning: If the instantaneous drop user has an instantaneous drop in the corresponding phase of the transformer total meter voltage at the moment of voltage reduction, the instantaneous drop data will be eliminated.

[0024] As a further preferred technical solution of the above technical solution, in step S3, the setting of risk dimensions includes:

[0025] Risk dimension 1 corresponding to the number of voltage drops: If one or more voltage drop events are detected within the preset time (one month) of the low-voltage user, the increased risk value = number of voltage drops * weight (the weight of the phase is capped when the number of voltage drops reaches 20 or more);

[0026] Risk dimension 2 corresponding to the voltage sag amplitude: When a low-voltage user determines that one or more voltage sag events occur, the voltage sag amplitude is the average value of multiple U differences (voltage differences). The increased risk value = U difference average value * weight (the weight of the phase with a voltage sag amplitude of 60 or more is capped);

[0027] Risk dimension 3 corresponding to theoretical resistance: Low voltage users judge one or more (n) voltage drop events, Increased risk value = theoretical resistance * weight (the weight of the phase with theoretical resistance of 20 or above is capped);

[0028] Risk dimension 4 corresponding to the correlation coefficient: The array composed of the voltage and current of the measuring points between the start and end of multiple voltage sag events of the user is included in the Pearson correlation coefficient function corr calculation, and the voltage and current correlation coefficient of the sag event is retained as a normalized value, between -1 and 1, and close to -1 is a negative correlation; if the correlation coefficient is negative and less than the preset value (-0.5), the voltage and current are highly negatively correlated, and the risk value is increased by |correlation coefficient|*weight (if the correlation coefficient reaches -1, the weight of this phase is capped);

[0029] Risk dimension 5 corresponding to low current at the moment of instantaneous drop: Calculate the current at the measuring point at the end of the user's voltage instantaneous drop event. If it is a single-phase user, determine whether it is less than the user's single-phase threshold (reported operating capacity / 2220*20%). If it is a three-phase user, determine whether the current at the moment when the voltage drops to the voltage low point is less than the user's three-phase threshold (reported operating capacity / 3 / 220*20%). If the above conditions are met, record the instantaneous drop as a low current instantaneous drop mark LI, and record the current Ilow at the moment of the instantaneous drop to the voltage point; if the proportion of LI marks in the instantaneous drop event is greater than the preset value (50%), increase the risk value = the proportion of LI marks in the user's instantaneous drop event * weight (if the proportion reaches 100%, the weight of this phase is capped);

[0030] Risk dimension 6 corresponding to the similarity of low current sag: If the user has multiple voltage sag events with low current sag flag LI, calculate the standard deviation of the one-dimensional array AIlow composed of multiple currents Ilow with low current sag flag LI. If the standard deviation is less than the preset value (preferably 0.5, which means that the fluctuation is small and the low current amplitude is close), it is judged that the oxidation and loose resistance are too large to cause the maximum current limit, and the user's low current sag similarity label SJXS is recorded; increase the risk value = (1-STD(AIlow))*weight (STD is the standard deviation function, STD reaches 0, then the weight of this phase is capped);

[0031] Risk dimension 7 corresponding to instantaneous large current drop and synchronous total meter instantaneous drop: If the current of the instantaneous drop user at the time of voltage reduction is greater than the current threshold (20A), it is attributed to line parameters and power supply radius, and the risk value is reduced;

[0032] Risk dimension 8 corresponding to instantaneous power outages: Collect whether users have submitted power outage information during the day. If so, record and mark the instantaneous power outage with TBTD, and count the number of synchronous power outages caused by instantaneous voltage drops in users. If the synchronous power outage with TBTD=1, the increased risk value=TBTDSL*weight (such users are generally severely oxidized or loose, and are prompted to be checked first).

[0033] Risk dimension 9 corresponding to the long power supply radius: For users whose power supply radius is too large and whose wire diameter is too thin, resulting in voltage drop, the theoretical impedance between the voltage drop user and the transformer is calculated through the line parameters and topology of the substation. If it is greater than the resistance threshold (such as 0.5 ohms), it is determined that the voltage drop is caused by the power supply radius and line parameters. It is recommended to cut over the user or replace the line. If the theoretical impedance calculated by the line parameters is greater than the resistance threshold, the risk value is reduced.

[0034] As a further preferred technical solution of the above technical solution, for step S3, the risk value calculation is specifically implemented as follows:

[0035] The AHP hierarchical analysis algorithm is used to quantify the oxidation and loosening risk values ​​of users who experience voltage sags. Each risk dimension is assigned a weight and the corresponding risk value is calculated: P is the risk dimension and ω is the weight value.

[0036] As a further preferred technical solution of the above technical solution, step S3 further includes step S4:

[0037] Users are sorted from large to small according to the risk value L, and the instantaneous drop time period is recorded. Users whose risk value is greater than the L threshold are pushed to the relevant object (equipment owner of the power supply company) (the equipment owner can also view the current and voltage curves and various risk dimensions through the system to further refine the judgment. The equipment owner goes to the site to confirm the meter connector in a targeted manner, and conducts preventive inspection and maintenance after confirmation). BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0039] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0040] In the preferred embodiments of the present invention, those skilled in the art should note that the electric power meter and the like involved in the present invention may be regarded as prior art.

[0041] Preferred embodiments.

[0042] The threshold values, preset values, etc. corresponding to the present invention are all reference values ​​and can be adjusted and changed according to actual needs.

[0043] The present invention discloses a method for quantitatively identifying oxidation of a meter joint based on voltage drop data, comprising the following steps:

[0044] Step S1: Obtain relevant information through the data center;

[0045] Step S2: Processing the relevant information to define low voltage users and voltage sag events, and cleaning the voltage sag events to remove relevant sag data;

[0046] Step S3: Set various risk dimensions to calculate the risk value of the meter connector. The input data is the voltage drop data of the user within a preset time (within 1 month), and the output is a quantitative risk value.

[0047] Specifically, step S1 is implemented as follows:

[0048] Obtaining first information including household number, household name, power supply station, address, power supply voltage, contact information, operating capacity, corresponding relationship between household number and meter, meter wiring method, meter maintainer information, and corresponding relationship between user and meter box through the user information table and the meter equipment information table;

[0049] Through the user's power load data table, obtain the second information including the A-phase, B-phase, and C-phase currents, A-phase, B-phase, and C-phase voltages, and the A-phase, B-phase, and C-phase apparent powers of the user's meter measurement points (96 measurement points in one day) (the measurement point data is an instantaneous value, the three-phase meter takes the three phases, and the single-phase meter takes the overlapped phases);

[0050] Obtain the corresponding relationship information of the user belonging to the substation through the electricity connection information table;

[0051] Through the user power outage information table, obtain the power outage and power restoration time information within the user's preset time (1 month).

[0052] More specifically, step S2 is implemented as follows:

[0053] First, traverse the user voltage data. When the voltage of one phase of the user is less than the threshold (215V), and at the same time, the voltage difference between the corresponding phase of the main meter of the area and the corresponding phase of the user is greater than the preset difference (10V), it is defined as a low-voltage user caused by the voltage difference. The user account number, voltage and current data of the day of occurrence are retained, updated daily, and historical data of the preset time (1 month) is retained;

[0054] When a certain phase of the user has low voltage, obtain the voltage at the low voltage moment and the maximum voltage of the a measurement points before the moment (preferably 3). If the voltage difference between the maximum voltage of the a measurement points before the low voltage moment and the voltage at the low voltage moment exceeds the preset difference (10V), it is defined as a voltage sag event of the user, and the start and end times of the voltage sag event are recorded, the voltage and current at the two times are recorded, and the voltage difference and current difference at the start and end of the voltage sag event are saved, where:

[0055] Ut-1~Ut-3 are the voltages of the first three measuring points, tmax is the moment when Ut-1~Ut-3 have their maximum values, and U difference is the instantaneous voltage drop difference;

[0056] Instantaneous voltage drop difference U difference = Max (Ut-1, Ut-2, Ut-3) - Ut;

[0057] If the voltage drop is greater than the threshold (10V), a voltage drop event of the user is recorded;

[0058] The instantaneous current drop difference I difference = |It-Itmax|.

[0059] Power outage data cleaning: If the user's voltage drops to near 0, and the current drops to near 0 at the same time, and the maximum current at the non-voltage drop time of the day is greater than the current threshold (user operating capacity / 220*0.2), the voltage drop is defined as caused by power outage, the user is marked as outage and the data of this drop is removed;

[0060] Phase loss data cleaning: If the low voltage value of the user's voltage drop and the voltage value of the corresponding phase of the substation main meter at that moment are both less than the voltage threshold (180V), it is defined as a phase loss causing a drop and the drop data is removed;

[0061] Multiple users with the same voltage drop cleaning: If multiple users have voltage drops at the same time under the same transformer (station area), it is defined as caused by branch lines or multi-household long power supply radius, and this batch of voltage drop data is eliminated;

[0062] Large current data cleaning: If the voltage of a user drops to a low voltage and the current is greater than the preset current (30A) during a transient voltage drop event, it is defined as a large current transient voltage drop and the transient voltage drop data is removed;

[0063] Instantaneous voltage recovery cleaning: record the current when the voltage drops to low voltage in a certain instantaneous voltage drop event of the user. If the voltage returns to normal within b measurement points (preferably 5) after the instantaneous voltage drop and the current at the normal voltage measurement point is greater than the current at the low voltage moment * threshold (preferably 0.8), it is defined as a instantaneous voltage drop not caused by connector oxidation and the instantaneous voltage drop data is discarded.

[0064] Transformer synchronous instantaneous drop cleaning: If the instantaneous drop user has an instantaneous drop in the corresponding phase of the transformer total meter voltage at the moment of voltage reduction, the instantaneous drop data will be eliminated.

[0065] Furthermore, in step S3, the setting of risk dimensions includes:

[0066] Risk dimension 1 corresponding to the number of voltage drops: If one or more voltage drop events are detected within the preset time (one month) of the low-voltage user, the increased risk value = number of voltage drops * weight (the weight of the phase is capped when the number of voltage drops reaches 20 or more);

[0067] Risk dimension 2 corresponding to the voltage sag amplitude: When a low-voltage user determines that one or more voltage sag events occur, the voltage sag amplitude is the average value of multiple U differences (voltage differences). The increased risk value = U difference average value * weight (the weight of the phase with a voltage sag amplitude of 60 or more is capped);

[0068] Risk dimension 3 corresponding to theoretical resistance: Low voltage users judge one or more (n) voltage drop events, Increased risk value = theoretical resistance * weight (the weight of the phase with theoretical resistance of 20 or above is capped);

[0069] Risk dimension 4 corresponding to the correlation coefficient: The array composed of the voltage and current of the measuring points between the start and end of multiple voltage sag events of the user is included in the Pearson correlation coefficient function corr calculation, and the voltage and current correlation coefficient of the sag event is retained as a normalized value, between -1 and 1, and close to -1 is a negative correlation; if the correlation coefficient is negative and less than the preset value (-0.5), the voltage and current are highly negatively correlated, and the risk value is increased by |correlation coefficient|*weight (if the correlation coefficient reaches -1, the weight of this phase is capped);

[0070] Risk dimension 5 corresponding to low current at the moment of instantaneous drop: Calculate the current at the measuring point at the end of the user's voltage instantaneous drop event. If it is a single-phase user, determine whether it is less than the user's single-phase threshold (reported operating capacity / 2220*20%). If it is a three-phase user, determine whether the current at the moment when the voltage drops to the voltage low point is less than the user's three-phase threshold (reported operating capacity / 3 / 220*20%). If the above conditions are met, record the instantaneous drop as a low current instantaneous drop mark LI, and record the current Ilow at the moment of the instantaneous drop to the voltage point; if the proportion of LI marks in the instantaneous drop event is greater than the preset value (50%), increase the risk value = the proportion of LI marks in the user's instantaneous drop event * weight (if the proportion reaches 100%, the weight of this phase is capped);

[0071] Risk dimension 6 corresponding to the similarity of low current sag: If the user has multiple voltage sag events with low current sag flag LI, calculate the standard deviation of the one-dimensional array AIlow composed of multiple currents Ilow with low current sag flag LI. If the standard deviation is less than the preset value (preferably 0.5, which means that the fluctuation is small and the low current amplitude is close), it is judged that the oxidation and loose resistance are too large to cause the maximum current limit, and the user's low current sag similarity label SJXS is recorded; increase the risk value = (1-STD(AIlow))*weight (STD is the standard deviation function, STD reaches 0, then the weight of this phase is capped);

[0072] Risk dimension 7 corresponding to large instantaneous current drops and synchronous total meter instantaneous drops: If the current of the instantaneous drop user at the time of voltage reduction is greater than the current threshold (20A), it is attributed to line parameters and power supply radius, and the risk value is reduced (reduced risk value = (current at the time of voltage reduction - 20) * weight);

[0073] Risk dimension 8 corresponding to instantaneous power outages: Collect whether users have submitted power outage information during the day. If so, record and mark the instantaneous power outage with TBTD, and count the number of synchronous power outages caused by instantaneous voltage drops in users. If the synchronous power outage with TBTD=1, the increased risk value=TBTDSL*weight (such users are generally severely oxidized or loose, and are prompted to be checked first).

[0074] Risk dimension 9 corresponding to the power supply radius being too long: For users whose power supply radius is too large and whose wire diameter is too thin, resulting in voltage drop, the theoretical impedance between the voltage drop user and the transformer is calculated through the line parameters and topology of the substation. If it is greater than the resistance threshold (such as 0.5 ohms), it is determined that the voltage drop is caused by the power supply radius and line parameters, and it is recommended to cut over the user or replace the line. If the theoretical impedance calculated by the line parameters is greater than the resistance threshold, the risk value is reduced (risk reduction = theoretical resistance * weight).

[0075] Furthermore, for step S3, the risk value calculation is specifically implemented as follows:

[0076] The AHP hierarchical analysis algorithm is used to quantify the oxidation and loosening risk values ​​of users who experience voltage sags. Each risk dimension is assigned a weight and the corresponding risk value is calculated: P is the risk dimension and ω is the weight value.

[0077] Preferably, step S3 further includes step S4:

[0078] Users are sorted from large to small according to the risk value L, and the instantaneous drop time period is recorded. Users whose risk value is greater than the L threshold are pushed to the relevant object (equipment owner of the power supply company) (the equipment owner can also view the current and voltage curves and various risk dimensions through the system to further refine the judgment. The equipment owner goes to the site to confirm the meter connector in a targeted manner, and conducts preventive inspection and maintenance after confirmation).

[0079] It is worth mentioning that the technical features such as the electric power meter involved in the patent application of this invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional methods in the field, and should not be regarded as the inventive point of the patent of this invention. The patent of this invention will not be further elaborated.

[0080] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A quantitative identification method for meter joint oxidation based on voltage sag data, characterized in that: The following steps are involved: Step S1: Obtain relevant information through the data center; Step S2: Processing the relevant information to define low voltage users and voltage sag events, and cleaning the voltage sag events to remove relevant sag data; Step S3: Set various risk dimensions to calculate the risk value of the meter connector. The input data is the voltage drop data of the user within a preset time, and the output is a quantitative risk value.

2. The method for quantitatively identifying oxidation of meter joints based on voltage sag data according to claim 1 is characterized in that: The specific implementation of step S1 is as follows: Obtaining first information including household number, household name, power supply station, address, power supply voltage, contact information, operating capacity, corresponding relationship between household number and meter, meter wiring method, meter maintainer information, and corresponding relationship between user and meter box through the user information table and the meter equipment information table; Obtaining second information including the currents of phase A, phase B, and phase C, the voltages of phase A, phase B, and phase C, and the apparent powers of phase A, phase B, and phase C at the user's meter measurement point through the user's power load data table; Obtain the corresponding relationship information of the user belonging to the substation through the electricity connection information table; Obtain the power outage restoration time information within the user's preset time through the user's power outage information table.

3. The method for quantitatively identifying oxidation of meter joints based on voltage sag data according to claim 2 is characterized in that: The specific implementation of step S2 is as follows: First, traverse the user voltage data. When the voltage of one phase of the user is less than the threshold, and at the same time, the voltage difference between the corresponding phase of the main meter in the area and the corresponding phase of the user is greater than the preset difference, it is defined as a low-voltage user caused by the voltage difference. The user account number, voltage and current data of the day of occurrence are retained, updated daily, and historical data of the preset time is retained; When a certain phase of the user has low voltage, the voltage at the low voltage moment and the maximum voltage of the a measurement points before the moment are obtained. If the voltage difference between the maximum voltage of the a measurement points before the low voltage moment and the voltage at the low voltage moment exceeds the preset difference, it is defined as a voltage sag event for the user, and the start and end times of the voltage sag event are recorded, the voltage and current at the two times are recorded, and the voltage difference and current difference at the start and end of the voltage sag event are saved; Power outage data cleaning: If the user's voltage drops to near 0, and the current drops to near 0 at the same time, and the maximum current at the non-voltage drop time of the day is greater than the current threshold, the voltage drop is defined as caused by a power outage, the user is marked as having a power outage, and the data for this drop is removed; Phase loss data cleaning: If the low voltage value of the user's voltage drop and the voltage value of the corresponding phase of the total meter of the substation at that moment are both less than the voltage threshold, it is defined as a phase loss causing the drop and the drop data is removed; Multiple users with voltage drop cleaning: If multiple users with the same transformer experience voltage drop at the same time, it is defined as caused by branch lines or multi-user long power supply radius, and this batch of voltage drop data will be removed; Large current data cleaning: If the voltage of a user drops to a low voltage and the current is greater than the preset current during a sudden drop event, it is defined as a large current sudden drop and the sudden drop data is removed; Instantaneous voltage recovery cleaning: record the current when the voltage drops to low voltage in a certain instantaneous voltage event of the user. If the voltage returns to normal within b measurement points after the instantaneous voltage drops and the current at the normal voltage measurement point is greater than the current at the low voltage moment * threshold, it is defined as a instantaneous voltage drop not caused by connector oxidation and the instantaneous voltage drop data is discarded. Transformer synchronous instantaneous drop cleaning: If the instantaneous drop user has an instantaneous drop in the corresponding phase of the transformer total meter voltage at the moment of voltage reduction, the instantaneous drop data will be eliminated.

4. The method for quantitatively identifying oxidation of meter joints based on voltage sag data according to claim 3 is characterized in that: In step S3, the risk dimension setting includes: Risk dimension 1 corresponding to the number of voltage drops: If one or more voltage drop events are detected within the preset time by the low voltage user, the increased risk value = number of voltage drops * weight; Risk dimension 2 corresponding to the voltage drop amplitude: When a low-voltage user determines that one or more voltage drop events occur, the voltage drop amplitude is the average value of multiple U differences, and the increased risk value = average value of U differences * weight; Risk dimension 3 corresponding to theoretical resistance: Low voltage users judge one or more voltage drop events, Increased risk value = theoretical resistance * weight; Risk dimension 4 corresponding to the correlation coefficient: The array composed of the voltage and current of the measuring points between the start and end of multiple voltage sag events of the user is included in the Pearson correlation coefficient function corr calculation, and the voltage and current correlation coefficient of the sag event is retained as a normalized value, which is between -1 and 1. If it is close to -1, it is negatively correlated; if the correlation coefficient is negative and less than the preset value, the voltage and current are highly negatively correlated, and the risk value is increased by |correlation coefficient|*weight; Risk dimension 5 corresponding to low current at the moment of instantaneous drop: Calculate the current of the measuring point at the end of the user's voltage instantaneous drop event. If it is a single-phase user, determine whether it is less than the user's single-phase threshold. If it is a three-phase user, determine whether the current at the moment when the voltage drops to the low voltage point is less than the user's three-phase threshold. If the above conditions are met, record the instantaneous drop as a low current instantaneous drop mark LI, and record the current Ilow at the moment of the instantaneous drop to the voltage point; if the proportion of LI marks in the instantaneous drop event is greater than the preset value, increase the risk value = the proportion of LI marks in the user's instantaneous drop event * weight; Risk dimension 6 corresponding to the similarity of low current sag: If the user has multiple voltage sag events with low current sag flag LI, calculate the standard deviation of the one-dimensional array AIlow composed of multiple currents Ilow with low current sag flag LI. If the standard deviation is less than the preset value, it is judged that the maximum current limit is caused by oxidation and loose resistance. Record the user's low current sag similarity label SJXS; increase the risk value = (1-STD(AIlow))*weight; Risk dimension 7 corresponding to instantaneous large current drop and synchronous total meter instantaneous drop: If the current of the instantaneous drop user at the time of voltage reduction is greater than the current threshold, it is attributed to line parameters and power supply radius, and the risk value is reduced; Risk dimension 8 corresponding to instantaneous voltage drop and synchronous power outage: collect whether the user has submitted power outage information within the day, if so, record and mark the instantaneous voltage drop and synchronous power outage flag TBTD, and count the number of synchronous power outages caused by instantaneous voltage drop of the user TBTDSL; if the synchronous power outage flag TBTD = 1, the increased risk value = TBTDSL * weight; Risk dimension 9 corresponding to the excessively long power supply radius: For users whose power supply radius is too large and whose wire diameter is too thin, resulting in voltage sag, the theoretical impedance between the user and the transformer is calculated based on the line parameters and topology of the substation. If it is greater than the resistance threshold, the voltage sag is judged to be caused by the power supply radius and line parameters. It is recommended to cut over the user or replace the line. If the theoretical impedance calculated by the line parameters is greater than the resistance threshold, the risk value is reduced.

5. The method for quantitatively identifying oxidation of meter joints based on voltage sag data according to claim 4 is characterized in that: For step S3, the risk value calculation is specifically implemented as follows: The AHP hierarchical analysis algorithm is used to quantify the oxidation and loosening risk values ​​of users who experience voltage sags. Each risk dimension is assigned a weight and the corresponding risk value is calculated: P is the risk dimension and ω is the weight value.

6. The method for quantitatively identifying oxidation of meter joints based on voltage sag data according to claim 5 is characterized in that: Step S3 further includes step S4: The users are sorted from large to small according to the risk value L, and the time period of instantaneous drop is recorded. The users whose risk value is greater than the L threshold are pushed to the relevant objects.

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