A method and device for monitoring the potential risk of burnout of a metering device

Abstract

The present disclosure discloses a method and device for monitoring potential risks of burnout of metering equipment, relating to the technical field of power systems. The method includes: identifying the monitored electric energy meter corresponding to the metering equipment to be monitored; obtaining data of the monitored electric energy meter to obtain monitored meter data, where the monitored meter data includes monitored meter voltage data and monitored meter current data; identifying the phase sequence of the monitored meter voltage data and / or the monitored meter current data to obtain a phase sequence identification result; according to the phase sequence identification result, extracting data corresponding to the target phase sequence from the monitored meter data to obtain first phase sequence data; and judging whether there are potential risks of burnout of the metering equipment according to the first phase sequence data. This method can not only accurately identify potential risks of burnout of metering equipment, but also help to locate the occurrence position of the burnout risks.

Description

Technical Field

[0001] The present disclosure generally relates to the field of power system technology. More specifically, the present disclosure relates to a method and device for monitoring the hidden danger of burning of metering equipment. Background Art

[0002] As the core hub between power supply and user power consumption, the safe and stable operation of metering equipment is of immeasurable value in ensuring the continuity and reliability of power supply. However, the frequent burning and failure of metering equipment not only directly leads to economic losses of power companies, but also affects the normal power consumption order of users. In severe cases, it may induce serious safety accidents such as fires, posing a major threat to the safety of public life and property. Therefore, it has become an urgent task to deepen the management of power safety, actively respond to and effectively curb the burning and failure of metering equipment and its potential hidden dangers.

[0003] In the prior art, the monitoring of safety hazards of metering equipment is mainly achieved by staff checking the operating data of the metering equipment. On the one hand, due to the large scale of the low-voltage user group, the amount of load curve data generated daily is extremely large, which not only leads to a heavy workload for maintenance personnel, but also causes the consumption of precious human and material resources. On the other hand, the safety hazards of low-voltage user metering equipment are deeply affected by multiple factors such as the on-site operating environment, user electricity usage habits, seasonal changes, and special events (such as holidays), showing significant gradual, random and sudden characteristics, which greatly increases the difficulty for grassroots personnel to promptly discover and quickly eliminate safety hazards. Therefore, how to build a set of early warning mechanisms that can accurately monitor and respond quickly to minimize the harm caused by safety hazards and avoid major safety accidents (such as fires) has become a major issue and challenge that the industry needs to solve urgently.

[0004] With the rapid popularization and application of IoT meters and smart meters version 2.0, as well as the continuous improvement of the informatization and automation levels of the new-generation marketing system 2.0 and the power consumption information collection system 2.0, it provides strong data support and technical foundation for the intelligent monitoring and analysis of potential safety hazards in the operation of metering equipment. In particular, with the comprehensive upgrade of the power consumption information collection system 2.0, the acquisition ability of the load curve data of low-voltage user electricity meters has achieved a qualitative leap in terms of density, frequency, integrity, and timeliness, laying a solid foundation for big data application analysis. This system has demonstrated powerful monitoring and early warning capabilities in the fields of power outage and restoration management, electricity theft detection, and intelligent operation and maintenance. By collecting the operation data of electricity meters, such as voltage, current, power, etc., reliable analysis of overload, abnormal voltage, and other situations can be achieved in combination with business experience, and early warning of potential overheating or burnout risks can be given. However, in terms of using multi-dimensional data characteristics and in-depth correlation analysis to identify potential burnout hazards of metering equipment, the domestic industry still lacks efficient and accurate analysis methods and technical means, and the potential in this field has not been fully released and explored.

[0005] In view of this, there is an urgent need to provide a method for monitoring potential burnout hazards of metering equipment, so as to effectively utilize the large amount of data collected by metering equipment, accurately identify potential burnout hazards of metering equipment, and thus prevent the occurrence of metering equipment burnout accidents. Summary of the Invention

[0006] In order to at least solve the problems described in the above background art section, the present disclosure proposes the following technical solutions and multiple embodiments thereof.

[0007] In a first aspect, the present disclosure proposes a method for monitoring potential burnout hazards of metering equipment, including: identifying the monitored electricity meter corresponding to the monitored metering equipment; obtaining the data of the monitored electricity meter to obtain monitored meter data, where the monitored meter data includes monitored meter voltage data and monitored meter current data; identifying the phase sequence of the monitored meter voltage data and / or the monitored meter current data to obtain a phase sequence identification result; according to the phase sequence identification result, extracting the data corresponding to the target phase sequence from the monitored meter data to obtain first-phase sequence data; and judging whether there are potential burnout hazards in the metering equipment according to the first-phase sequence data.

[0008] In a second aspect, the present disclosure proposes a device for monitoring potential burnout hazards of metering equipment, including: a processor configured to execute program instructions; and a memory configured to store the program instructions, and when the program instructions are loaded and executed by the processor, the device executes the method according to the first aspect. Brief Description of the Drawings

[0009] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present disclosure will become readily understandable. In the drawings, several embodiments of the present disclosure are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0010] Figure 1 An exemplary schematic diagram showing the connection relationship of a metering device in a power supply substation area in some embodiments of the present disclosure.

[0011] Figure 2 An exemplary flowchart showing a method for monitoring potential risks of burnout of a metering device in some embodiments of the present disclosure.

[0012] Figure 3 An exemplary concept explanation diagram for reachable distance.

[0013] Figure 4 An exemplary flowchart showing a method for determining whether there is a potential risk of burnout of a metering device based on first-phase sequence data in some embodiments of the present disclosure.

[0014] Figure 5 An exemplary flowchart showing a method for determining whether there is a potential risk of burnout of a metering device based on first-phase sequence data in some other embodiments of the present disclosure.

[0015] Figure 6 An exemplary flowchart showing a method for determining whether there is a potential risk of burnout of a metering device based on first-phase sequence data and reference table phase sequence data in some embodiments of the present disclosure.

[0016] Figure 7 An exemplary interpretation diagram of neutral point drift is shown.

[0017] Figure 8 An exemplary schematic diagram showing that neutral point drift causes drift of the phase sequence voltage and current angle is shown.

[0018] Figure 9 An exemplary circuit diagram of voltage sampling of a metering chip of an electric energy meter is shown.

[0019] Figure 10 A block diagram showing the hardware configuration of a device 100 that can implement the method for monitoring potential risks of burnout of a metering device according to the embodiments of the present disclosure is shown. Detailed implementation manners

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0021] It should be understood that the terms "including" and "comprising" used in the specification and claims of the present disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0022] It should also be understood that the terms used in the specification of the present disclosure are merely for the purpose of describing specific embodiments and are not intended to limit the present disclosure. As used in the specification and claims of the present disclosure, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in the specification and claims of the present disclosure refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] As used in this specification and the claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0024] The following will describe in detail the specific embodiments of the present disclosure in conjunction with the accompanying drawings.

[0025] In a power system, metering equipment is a series of devices used to measure and record power usage, covering key components such as electricity meters, transformers, combined wiring boxes, knife switches, and wires.

[0026] Figure 1 An exemplary schematic diagram showing the connection relationship of metering equipment in a power supply substation area in some embodiments of the present disclosure is shown. As Figure 1 shown, a three-phase power source that can be a three-phase generator or transformer outputs stable and efficient three-phase electricity; the three-phase electricity consists of three alternating currents with the same frequency, equal amplitude, and a phase difference of 120° in sequence, and is transmitted by live wires, corresponding respectively Figure 1The phase A live wire, phase B live wire, and phase C live wire in it. The live wire can also be called the phase wire; the neutral wire, which can also be called the neutral conductor, has a potential of zero under ideal conditions and is used to form a power supply circuit with the live wire.

[0027] Such as Figure 1 The power supply system shown includes a substation master meter and user electricity meters. Among them, the substation master meter is used to measure the electricity consumption of the entire substation area, while the user electricity meters (such as Figure 1 single-phase electricity meter A, three-phase electricity meters B, and C in it) are used to measure the amount of electrical energy consumed by each or part of the users. In this power supply system, a single-phase power supply can be drawn from any live wire and the neutral wire to supply power to single-phase loads; for three-phase loads, a three-phase power supply can be drawn from the three live wires and the neutral wire to supply power to three-phase loads. Corresponding to single-phase loads and three-phase loads, user electricity meters can be divided into single-phase electricity meters and three-phase electricity meters.

[0028] Continue Figure 1 , in some embodiments, on the single-phase power supply circuit composed of any live wire and the neutral wire, knife switches S1, S2, and single-phase electricity meter A can be provided. In some other embodiments, on the three-phase power supply circuit composed of the three live wires and the neutral wire, knife switches S3, S4, and three-phase electricity meter B can be provided. For the knife switch, it is used to control the on-off of the circuit. In equipment maintenance, troubleshooting, or emergencies, the power supply can be quickly cut off to ensure the safety of personnel and equipment. In some embodiments, whether it is a single-phase power supply circuit or a three-phase power supply circuit, a combined wiring box can also be provided. The combined wiring box is used to reliably connect the electricity meter, transformer, and power line together to ensure the accurate transmission of electrical signals and prevent the influence of external factors on the metering equipment. In some embodiments, transformers can be provided in the three-phase power supply circuit. The transformers are divided into voltage transformers and current transformers, which are used to transform high voltage and large current into low voltage and small current for the safe operation of measurement and protection equipment.

[0029] In a power supply substation area, a large number of low-voltage users can be supplied with power. For example, all households in a community share a substation area, and all offices in an office building share a substation area. Each low-voltage user has a set of specific metering equipment corresponding to it to ensure accurate metering of the electrical energy consumption of all users. Thus, for the power supply system as Figure 1 shown, the specified number of metering equipment can be set according to actual needs and the number of low-voltage users.

[0030] As the usage time increases, various components of the metering device gradually age, and the insulation performance deteriorates, making it prone to problems such as electric leakage and short - circuit, thus triggering burnout faults. According to the electrical information collected by the electricity meter in the metering device, the burnout hidden danger of the metering device can be monitored. The inventors of the present disclosure found that: in existing user electricity meters, among the data collected by single - phase electricity meters, the phase sequence by default corresponds to phase A of the substation master meter; among the data collected by three - phase electricity meters, phases A, B, and C by default correspond to phases A, B, and C of the substation master meter respectively. However, in reality, the corresponding relationship between the phase sequence of the data collected by the user electricity meter and the phase sequence of the substation master meter does not necessarily conform to this default value. This non - correspondence between the phase sequence of the collected data and the actual physical phase lines results in the inability to effectively determine which part of the power - supply phase line of the metering device has failed, nor can the collected data of multiple electricity meters be comprehensively used to monitor the burnout hidden danger of the metering device. Exemplarily, for 10 three - phase electricity meters under a power - supply substation area, when the corresponding relationship between the phase sequence of the three - phase collected data of each electricity meter and phases A, B, and C of the substation master meter is unclear, the phase - sequence corresponding relationship among the collected data of these 10 three - phase electricity meters is also unclear, thus making it impossible to align the three - phase data collected by these electricity meters, let alone conduct in - depth data analysis.

[0031] In view of this, the present disclosure provides a method for monitoring the burnout hidden danger of a metering device, so as to effectively utilize the big data collected during the operation of smart electricity meters and accurately identify the burnout hidden danger of the metering device.

[0032] Figure 2 The exemplary flowchart of the method for monitoring the burnout hidden danger of a metering device in some embodiments of the present disclosure is shown. As Figure 2 shown, the method includes: Step 201, confirm the monitored electricity meter corresponding to the monitored metering device. Step 202, obtain the data of the monitored electricity meter to obtain monitored - meter data, where the monitored - meter data includes monitored - meter voltage data and monitored - meter current data. Step 203, identify the phase sequence of the monitored - meter voltage data and / or the monitored - meter current data to obtain a phase - sequence identification result. Step 204, according to the phase - sequence identification result, extract the data corresponding to the target phase sequence from the monitored - meter data to obtain first - phase - sequence data. Step 205, judge whether the metering device has a burnout hidden danger according to the first - phase - sequence data.

[0033] Regarding Step 201, it can be understood that in a power - distribution system that supplies power to multiple low - voltage users, there are a large number of metering devices, and each set of metering devices has a corresponding electricity meter. The monitored metering device refers to the metering device for which it is necessary to judge whether it has a burnout hidden danger. Correspondingly, the electricity meter corresponding to the monitored metering device is the monitored electricity meter.

[0034] For step 202, it can be understood that the monitored meter data may include operation data. The operation data is time-series data generated during the actual operation of the electricity meter, including active power data, voltage data, current data, electricity consumption data, substation line loss rate data, etc. The method for obtaining the operation data can be to obtain each item of operation data at fixed time intervals. For example, in the 24 hours of each day, real-time data such as active power, current, and voltage can be obtained simultaneously at each whole hour, so that operation data at 24 time points can be obtained every day; in this case, the active power, current, voltage, electricity consumption, and substation line loss rate data can form multi-variable time-series data. In addition, the obtained operation data includes historical operation data of the monitored electricity meter in the most recent week, the most recent three months, or the most recent year, etc.

[0035] Continuing with step 202, in some embodiments, the data of the monitored electricity meter may further include: archive data in the new generation power consumption information collection 2.0 system and the marketing 2.0 system, user GIS coordinate data, and user electricity meter box-meter relationship data; among them, the archive data is mainly static information about the attributes and installation and use of the electricity meter itself, such as substation capacity, comprehensive multiplier, substation type, user information, metering point information, and electricity meter information; the electricity meter information can further include the electricity meter model, manufacturer, installation time, and asset number.

[0036] For step 203, it can be understood that the input voltage of a single-phase electricity meter is provided by any one phase wire of the substation. Identifying the phase sequence of the single-phase electricity meter to obtain a phase sequence identification result is to determine whether the data collected by the electricity meter corresponds to phase A, phase B, or phase C in the three-phase electricity of the substation main meter. The input voltage of a three-phase electricity meter is provided by three phase wires. Identifying the phase sequence of the three-phase electricity meter to obtain a phase sequence identification result is to determine the corresponding relationship between the three-phase data collected by the electricity meter and phase A, phase B, and phase C in the three-phase electricity of the substation main meter. The voltage and current data of the monitored electricity meter both carry phase sequence information. Therefore, the phase sequence identification result can be obtained based on the voltage data or the current data respectively, or by combining the voltage data and the current data.

[0037] For step 204, it can be understood that since a single-phase electricity meter has only one phase, its first-phase sequence data is the monitored meter data itself. For a three-phase electricity meter, its electricity meter data includes data corresponding to the three phases respectively. The target phase sequence can be any one phase, two phases, or three phases of the three phases. After obtaining the phase sequence identification result, the phase sequence data corresponding to the target phase sequence can be accurately extracted from the electricity meter data. In this case, the first-phase sequence data can be data corresponding to at least one phase of the three phases.

[0038] Thus, the method for monitoring and measuring the potential damage of a metering device provided in this disclosure can extract the phase sequence data of the monitored meter corresponding to the phase sequence of the main meter of the distribution transformer by identifying the phase sequence of the electric energy meter of the monitored metering device. Since the identification of potential damage is carried out on the phase sequence data of the monitored meter, it is possible to determine whether there is a potential damage in the power supply circuit corresponding to the target phase sequence, thereby not only accurately identifying the potential damage of the metering device, but also helping to locate the position where the potential damage occurs.

[0039] In some embodiments, collecting data and identifying the phase sequence for a certain phase of a single-phase electric energy meter or a three-phase electric energy meter includes calculating according to the following formula (1): for the single-phase current or voltage data x of the electric energy meter, which phase among the three phases A, B, and C of the main meter of the distribution transformer it corresponds to.

[0040]

[0041] In formula (1), z i represents the voltage or current data of a certain phase in the main meter of the distribution transformer. It can be understood that when x is voltage data, z i is voltage data; when x is current data, z i is current data. Cov(z i , x) represents calculating the covariance of z i and x, and Var(z i ), Var(x) respectively represent calculating the variance of z i , x. Then represents the correlation coefficient between the voltage or current data of a certain phase in the main meter of the distribution transformer and the single-phase voltage or current data of the user's electric energy meter. Here, the phase with the largest correlation coefficient is taken as the phase in the main meter of the distribution transformer corresponding to the single-phase collected data of the user's electric energy meter.

[0042] Exemplarily, the method for calculating the correlation coefficient between the A-phase voltage data in the collected data of the user's electric energy meter and the voltage data of each phase in the main meter of the distribution transformer can be:

[0043]

[0044] where, P Uza,Uha represents the correlation coefficient between the A-phase voltage Uza of the main meter of the distribution transformer and the A-phase voltage Uha of the user; P Uzb,Uha represents the correlation coefficient between the B-phase voltage Uzb of the main meter of the distribution transformer and the A-phase voltage Uha of the user; P Uzc,UhaRepresents the correlation coefficient between the phase C voltage Uzc of the total meter of the substation and the phase A voltage Uha of the user. Cov(Uza,Uha) represents the covariance between the phase A voltage Uza of the total meter of the substation and the phase A voltage Uha of the user; Cov(Uzb,Uha) represents the covariance between the phase B voltage Uzb of the total meter of the substation and the phase A voltage Uha of the user; Cov(Uzc,Uha) represents the covariance between the phase C voltage Uza of the total meter of the substation and the phase A voltage Uha of the user; Var(Uza), Var(Uzb), Var(Uzc), Var(Uha) represent the variances of Uza, Uzb, Uzc and Uha respectively; P Uza,Uha , P Uzb,Uha , P Uzc,Uha The phase sequence of the total meter voltage in the substation corresponding to the maximum value of the three values ​​is the actual phase sequence of phase A of the user.

[0045] The phase sequence identification of phase B and phase C in the user's electric energy meter data is the same as the above-mentioned phase A identification process, and the phase sequence can also be identified by replacing the voltage data with current data. For example, for a three-phase user electric energy meter, denoted as EEM (Electric Energy Meter), it is assumed that the A, B, and C three-phase collected data of EEM correspond to A, B, and C of the substation total meter respectively. However, using the 7-day voltage data of the electric energy meter in the substation, the correlation coefficients of the A, B, and C three-phase collected data of EEM and the A, B, and C three-phase of the substation total meter are calculated respectively, and the results are shown in Table 1. Among them, the phase with the highest correlation coefficient with EEM's phase A is phase B in the substation master table, the phase with the highest correlation coefficient with EEM's phase B is phase C in the substation master table, and the phase with the highest correlation coefficient with EEM's phase C is phase A in the substation master table. Therefore, the phase sequence identification result of EEM is: the collected data of EEM's phase A corresponds to phase B in the substation master table, the collected data of EEM's phase B corresponds to phase C in the substation master table, and the collected data of EEM's phase C corresponds to phase A in the substation master table.

[0046]

[0047] Table 1

[0048] In some embodiments, acquiring the data of the monitored electric energy meter to obtain the monitored meter data includes: acquiring the original data of the monitored electric energy meter; performing abnormal value detection and / or missing value interpolation on the original data to obtain the monitored meter data.

[0049] It is understandable that the raw data is the data directly obtained from the electric energy meter, which is generated by the electric energy meter during operation. However, due to the communication problems between the terminal and the meter, there are abnormal data in the raw data. Before using the electric energy meter data to identify the hidden dangers of burning of the metering equipment, the abnormal values ​​need to be processed.

[0050] In some embodiments, a univariate outlier detection method based on Grubbs' Test is used to detect and remove outliers from the univariate time series in the original data. It can be understood that the variable refers to a physical quantity collected by the electric energy meter, such as current, voltage, or power, and the univariate can be, for example, the current of a certain phase, the voltage of a certain phase, or the power of a certain phase, etc.

[0051] Grubbs' Test is a hypothesis testing method often used to test for a single outlier in a univariate data set that follows a normal distribution. If there is an outlier, it must be the maximum or minimum value in the data set. The null hypothesis and alternative hypothesis are as follows: H0: There is no outlier in the data set; H1: There is one outlier in the data set. The algorithm flow for performing outlier detection on the univariate time series x using Grubbs' Test is as follows:

[0052] 1. Calculate the mean of the univariate time series standard deviation s, minimum value min, and maximum value max;

[0053] 2. Calculate the differences between min, max, and mean respectively. The one with a larger difference (min or max) is the suspected value;

[0054] 3. Calculate the standard score of the suspected value where x_s is the suspected value. If z_score is greater than the Grubbs critical value, then this suspected value is an outlier point. The Grubbs critical value is obtained by looking up a table.

[0055] In some embodiments, a method based on the Local Outlier Factor (LOF) is used to detect and remove outliers from the multivariate time series in the original data. It can be understood that for single-phase or three-phase electric energy meters, the multivariate consists of variables such as single-phase current, voltage, and power. LOF is based on density analysis and detects outliers through the local data density. The LOF method mainly determines whether a point p is an outlier by comparing the density of each point (p) with the density of its neighboring points. The lower the density of point p, the higher the probability that point p is an outlier. In the LOF method, density is calculated based on the distance between points. The farther the distance between points, the lower the density; the closer the distance, the higher the density. The LOF method calculates density through the k-neighborhood of a point. Since it does not calculate density based on all points globally, it is called the "local outlier factor".

[0056] The LOF algorithm calculates an outlier factor LOF for each point in the dataset. By judging whether LOF is close to 1, it determines whether it is an outlier factor. If LOF is much greater than 1, it is considered an outlier factor; if LOF is close to 1, it is considered a normal point. The following introduces the important concepts related to calculating the outlier factor in the LOF algorithm:

[0057] ① k-distance of point p: Sort the distances between other points and point p from small to large. The distance of the k-th closest point to point p is the k-distance of point p.

[0058] ② k-distance neighborhood of point p: The set of points whose distance to point p is less than or equal to the k-distance.

[0059] ③ Reachability distance of point p relative to point o: The k-th reachability distance of point p to point o = max(k-distance of o's k-nearest neighbor k_distance(o), distance from point p to point o ‖p - o‖).

[0060] Figure 3 For an exemplary concept explanation diagram of reachability distance, as Figure 3 shown, for different points, their k-th reachability distances to point o are calculated differently. Figure 3 In, the k-th reachability distance from point p1 to point o is reach-dist k (p1, o). The dashed circle is the k-nearest neighbor distance of point o (i.e., the distance from the k-th nearest point to point o, where k is 3 here). Since the distance from p1 to o is less than the k-nearest neighbor distance of point o, then reach-dist k (p1, o) = k-distance(o); the distance from point p2 to o ‖p2 - o‖ is greater than k-distance(o), then the k-th reachability distance from point p2 to point o, reach-dist k (p2, o), is the distance from point p2 to point o ‖p2 - o‖.

[0061] ④ Local reachability density. As shown in formula (2), the local reachability density of point p is defined as the reciprocal of the average reachability distance of p's k-nearest neighbor points (i.e., the reciprocal of the average of all reachability distances within the k-distance neighborhood of point p). The larger this value, the more compact the data clustering.

[0062]

[0063] In formula (2), lrd k (p) is the local reachability density of point p, k is the number of nearest neighbor points, and reach-dist k (p, o) is the reachability distance from each point o in N k (p) within the k-distance neighborhood of point p to point p.

[0064] ⑤ Local Outlier Factor. As shown in formula (3), the Local Outlier Factor of point p is the value of the local reachability density of points in the neighborhood divided by the local reachability density of point p. The magnitude of the Local Outlier Factor (LOF) represents the credibility of the point being an outlier. That is, the larger the factor, the more likely the point is an outlier.

[0065]

[0066] In formula (3), LOF k (p) is the Local Outlier Factor of point p, and lrd k (o) is the local reachability density of each point o in N k (p) within the k-distance neighborhood of point p.

[0067] In the data collection of the power consumption information acquisition system, data loss is an inevitable and unavoidable problem. In particular, the loss of data such as voltage, current, and power will directly affect the accuracy of mining and predicting abnormal clues of operation safety hazards. Therefore, the processing of missing data becomes very important.

[0068] In some embodiments, the missing attribute is taken as the dependent variable, and other attributes are taken as independent variables, and the nth-degree polynomial approximation method is used for missing value imputation. The imputation process is as follows:

[0069] The first step is to select a 3rd-order (or 5th-order) fitting equation. Let the function fitting equation between the time series data (such as voltage and current) x and the attribute time t be:

[0070] x(t i ) = a0 + a1t i + a2t i 2 + a3t i 3 (4)

[0071] The second step is to select the first N and the last N data of the time series data [x n , t n : Substitute [x i , t i into the fitting equation, and solve for a0, a1, a2, and a3 as the undetermined coefficients of the fitting equation.

[0072] The third step is to make the mean square error of the imputed [x n , t n value reach the minimum according to the least squares principle, and solve for the values of the coefficients a0, a1, a2, and a3, that is, solve the mean square error equation as shown in formula (5):

[0073]

[0074] Establish matrix calculations for coefficients a0, a1, a2, and a3 as shown in formula (6):

[0075]

[0076] Denote formula (6) as TA = X, then the following derivation can be carried out to solve for the coefficients a0, a1, a2, and a3 of the fitting equation (4):

[0077]

[0078]

[0079] In the fourth step, optimize the function structure of equation (4) and input the value t of the missing attribute parameter n , and calculate the missing attribute data [x n , t n according to formula (7):

[0080] x(t n ) = a0 + a1t n + a2t n 2 + a3t n 3 = [(a3t n + a2)t n + a1]t n + a0 (7)

[0081] Figure 4 FIG. shows an exemplary flowchart of a method for determining whether a metering device has a burnout hazard based on first-phase sequence data in some embodiments of the present disclosure. It can be understood that Figure 4 is a specific implementation of the above step 205. As Figure 4 shown, in some embodiments, determining whether the metering device has a burnout hazard based on the first-phase sequence data includes: Step 401, extracting data from the first-phase sequence data according to the load of the monitored electric energy meter to obtain second-phase sequence data. Step 402, calculating the loop resistance of the monitored electric energy meter in the target phase sequence according to the second-phase sequence data. Step 403, determining whether the metering device has a burnout hazard according to the discreteness of the loop resistance.

[0082] The load of the electricity meter can be measured by the current and power data of the electricity meter. As mentioned before, the data of the monitored meter can be multivariate time-series data. Then, for the data at each moment, the load of the electricity meter at that moment can be measured according to its current or power value. Thus, according to the load of the electricity meter, a data subset with a high load approximation degree can be extracted from the first phase sequence data to obtain the second phase sequence data. In some embodiments, extracting a data subset with a high load approximation degree from the first phase sequence data to obtain the second phase sequence data includes the following steps:

[0083] First, filter out the valid data with electrical load from the first phase sequence data (D1) according to the following formula (8).

[0084] E = {D1 t |I t *K ≥ 1} (8)

[0085] In formula (8), I t represents the absolute value of the current magnitude at time t in D1, with the unit of ampere (A). K is the comprehensive multiplier of the electricity meter, which is the multiple multiplied when the electricity meter measures electricity and is determined by the product of the current transformer ratio and the voltage transformer ratio. According to formula (8), the data of the time points with smaller loads can be filtered out from the first phase sequence data, so as to retain the data of the time points with effective loads and obtain the first phase sequence effective data (E).

[0086] Then, sort the first phase sequence effective data according to the absolute value of the current value, and select a data subset with a high load similarity from it to obtain the second phase sequence data (D2). Among them, the similarity of the load is measured according to the deviation rate of the absolute value of the current data as shown in formula (9).

[0087]

[0088] In formula (9), Is represents the current data in D2, |Is| represents the absolute value of the current data in D2, and max(x) and min(x) respectively represent the maximum value and the minimum value of x. According to formula (9), the second phase sequence data with a high load similarity can be extracted from the first phase sequence effective data.

[0089] For step 402, it can be understood that the second phase sequence data includes voltage data (Us) and current data (Is). Based on Ohm's law, the loop resistance at that moment can be calculated according to the voltage data and current data at the same moment. Thus, the loop resistance (Rs) is calculated for the second phase sequence data using formula (10) as the loop resistance of the monitored electricity meter in the target phase sequence.

[0090]

[0091] The inventors of the present disclosure have found that for metering devices without the risk of burnout, under working conditions with similar loads, the loop resistance value is basically stable and unchanged; while for metering devices with the risk of burnout, even under working conditions with similar loads, before and after the occurrence of the burnout risk, there will be a mutation inflection point in the loop resistance value, that is, the loop resistance value fluctuates. Therefore, it is possible to determine whether a metering device has a burnout risk based on the discreteness of the loop resistance value. In some embodiments, the discreteness (S) of the loop resistance Rs is calculated according to the following formula (11).

[0092]

[0093] In formula (11), n represents the number of time points included in the second phase sequence data, and μ represents the mean value of Rs.

[0094] In some embodiments, a discreteness threshold can be set. By comparing the calculated discreteness value of Rs with the discreteness threshold, it is possible to determine whether a metering device has a burnout risk. In some embodiments, the discreteness threshold can be set to 1. When S > 1, it is determined that the loop resistance value has a certain degree of volatility, indicating that there is a burnout risk in the metering device loop.

[0095] The inventors of the present disclosure have found that under a power supply substation area, each phase of the substation master meter needs to supply power to multiple electricity meters. For the data of a certain phase sequence of the monitored electricity meter, the data of other electricity meters in the same phase sequence in the same substation area can be referred to determine whether the phase sequence data of the monitored electricity meter is abnormal, and thus determine whether the monitored metering device has a burnout risk. Exemplarily: when the data of other electricity meters in the target phase sequence are relatively close, and only the data of the monitored electricity meter in the target phase sequence shows an outlier, there is a high probability that the monitored electricity meter has a burnout risk.

[0096] Figure 5 An exemplary flowchart of a method for determining whether a metering device has a burnout risk based on the first phase sequence data in some other embodiments of the present disclosure is shown. It can be understood that Figure 5 is another specific implementation manner of the above step 205. As Figure 5 shown, in some embodiments, according to the first phase sequence data, determining whether the metering device has a burnout risk includes: step 501, the data of the monitored meter further includes the location data of the monitored meter. According to the location data of the monitored meter and the load of the monitored meter, at least two reference electricity meters are determined. Step 502, obtaining the data of the reference electricity meters in the target phase sequence to obtain the reference meter phase sequence data. Step 503, determining whether the metering device has a burnout risk according to the first phase sequence data and the reference meter phase sequence data.

[0097] For step 501, it can be understood that based on the monitored meter location data and the load of the monitored meter, a reference meter that is adjacent to the monitored meter in location and has a similar or identical load can be determined for the monitored electricity meter. Herein, the loads of two electricity meters being similar can mean that within a specific time range or at a specific moment, the load difference between the two electricity meters is small. In some embodiments, in order to determine at least two reference meters for the monitored electricity meter, a set of electricity meters adjacent in location to the monitored electricity meter can first be determined, and then electricity meters with similar loads can be extracted from the set of adjacent electricity meters as the reference meters. In other embodiments, a set of electricity meters with similar loads can first be determined for the monitored electricity meter, and then electricity meters adjacent in location can be extracted from the set of electricity meters with similar loads as the reference meters.

[0098] In some embodiments, the distance between electricity meters is calculated based on the longitude and latitude coordinate data of the meter box GIS using the Haversine formula. The Haversine formula is a formula used to calculate the great circle distance between two points on the earth and is widely used in fields such as geographic information systems, navigation, and map making due to its simplicity and accuracy. Its calculation method can be described by formula (12):

[0099]

[0100] In formula (12), d represents the great circle distance between two points. The coordinates of the two points are A(lat1, lon1) and B(lat2, lon2) respectively. lat1 and lat2 are latitudes (in radians), lon1 and lon2 are longitudes, and r is the average radius of the earth, with a value of 6371 kilometers. In some embodiments, a distance threshold can be set, and other electricity meters with a distance less than the set threshold from the monitored electricity meter can be regarded as adjacent electricity meters. As for the value of the distance threshold, it can be 10m.

[0101] In the above embodiments, adjacent electricity meters are determined for the monitored electricity meter based on the longitude and latitude coordinate data of the meter box GIS. In other embodiments, adjacent electricity meters are determined for the monitored electricity meter according to the box-meter relationship in the marketing field operation system. Under a power supply area, a distribution box usually connects multiple electricity meters, and then the electricity meters connected to the same distribution box can be considered adjacent electricity meters. In other embodiments, adjacent electricity meters are determined for the monitored electricity meter by comprehensively considering the longitude and latitude coordinate data of the meter box GIS and the box-meter relationship.

[0102] In some embodiments, similar to the method for obtaining the first phase sequence data, obtaining the reference table phase sequence data includes: obtaining the data of the reference watt-hour meter to obtain the reference table data; identifying the phase sequence of the reference table voltage data and / or current data to obtain the reference table phase sequence identification result; and extracting the data corresponding to the target phase sequence from the reference table data according to the reference table phase sequence identification result to obtain the reference table phase sequence data. This part has been introduced in sufficient detail in the foregoing text and will not be elaborated here.

[0103] Regarding step 503, it can be understood that the positions and loads of the monitored watt-hour meter and the reference watt-hour meter are similar. In the case where there is no potential risk of burnout of the metering device, the data of each watt-hour meter on the same phase sequence should also be relatively close. For a metering device with a potential risk of burnout, its phase sequence data will show outlier characteristics. Therefore, it is possible to determine whether there is a potential risk of burnout of the metering device based on the first phase sequence data and the second phase sequence data.

[0104] Figure 6 The exemplary flowchart of the method for determining whether there is a potential risk of burnout of a metering device based on the first phase sequence data and the reference table phase sequence data in some embodiments of the present disclosure is shown. It can be understood that Figure 6 is a specific implementation manner of the above step 503. As Figure 6 shown, in some embodiments, determining whether there is a potential risk of burnout of the metering device based on the first phase sequence data and the reference table phase sequence data includes: step 601, calculating the contact resistance difference between the monitored watt-hour meter and the reference table at a set moment according to the first phase sequence data and the reference table phase sequence data to obtain the first contact resistance difference. Step 602, calculating the contact resistance difference between the reference watt-hour meters at a set moment according to the reference table phase sequence data to obtain the second contact resistance difference. Step 603, determining whether there is a potential risk of burnout of the metering device according to the first contact resistance difference and the second contact resistance difference.

[0105] It can be understood that the loop impedance of the user watt-hour meter mainly consists of the user load resistance, the contact resistance, and the wire resistance between the area total meter and the user watt-hour meter. The contact resistance among them reflects the contact condition of the wires in the metering device. A high contact resistance means poor wire contact, thus there is a potential risk of burnout. For two adjacent watt-hour meters, the wire resistance from the area total meter to each watt-hour meter is almost the same. When the user load resistances are the same or close, the difference in loop resistance of the same phase sequence is the contact resistance difference. In this way, the loop resistances of the monitored watt-hour meter and the reference watt-hour meter at the same set moment can be calculated respectively according to the first phase sequence data and the reference table phase sequence data, and then the loop resistance of the monitored watt-hour meter is subtracted from the loop resistance of the reference watt-hour meter to obtain the first contact resistance difference; the loop resistances of different reference watt-hour meters are subtracted to obtain the second contact resistance difference.

[0106] For step 603, it can be understood that the first contact resistance difference reflects the difference in contact resistance between the monitored electricity meter and the reference electricity meter, while the second contact resistance difference reflects the difference in contact resistance between the reference electricity meters. When the difference in contact resistance between the reference electricity meters is small and the difference in contact resistance between the monitored electricity meter and the reference electricity meters is large, it can be determined that the contact resistance value of the monitored electricity meter is an outlier, that is, the monitored electricity meter has a fault. In some embodiments, a threshold can be set for the contact resistance difference. When the maximum value of the second contact resistance difference is less than the set contact resistance difference threshold and the minimum value of the first contact resistance difference is greater than the set contact resistance difference threshold, it is determined that there is a potential risk of burnout for the monitored metering device.

[0107] Exemplarily, taking low-voltage user A as an example, whose electricity meter number is XX3368, the acquisition data of phase A of user A's electricity meter on a certain day is selected for analysis, and the data shown in Table 2 is obtained. As can be seen from Table 2, 21:00 is the moment with the lowest load of user A's electricity meter, and 20:00 is the moment with the highest load of user A's electricity meter. However, at 20:00, there is only 1 user adjacent to user A's location and with a similar load, which cannot provide a reference for judging whether the contact resistance of user A's electricity meter is abnormal. At 21:00, there are 3 users adjacent to user A's location and with similar loads, corresponding to electricity meters XX6288, XX2801, and XX5428 respectively. These three electricity meters constitute the reference electricity meters for user A's electricity meter. Extract the phase A data of the three reference electricity meters at 21:00, as shown in formulas (13) and (14), and calculate the loop resistance of user A's electricity meter and the reference electricity meters according to Ohm's law.

[0108]

[0109] Among them, represents the loop resistance value of user A's electricity meter at time t, represents the voltage value of user A's electricity meter at time t, represents the current value of user A's electricity meter at time t; k is used to represent the kth reference electricity meter of user A's electricity meter at time t, and the value ranges from 1, 2, 3,...; represents the loop resistance value of the kth reference electricity meter at time t, represents the voltage value of the kth reference electricity meter at time t, represents the current value of the kth reference electricity meter at time t.

[0110] Then, calculate the contact resistance difference between user A's electricity meter and the reference electricity meters according to the following formula (15), and calculate the contact resistance difference between the reference electricity meters according to formula (16).

[0111]

[0112] Among them, represents the contact resistance difference between the electricity meter of User A and the k-th reference electricity meter at time t; represents the contact resistance difference between the i-th reference electricity meter and the j-th reference electricity meter at time t; respectively represent the loop resistances of the i-th and j-th reference electricity meters at time t; respectively represent the voltages of the i-th and j-th reference electricity meters at time t; respectively represent the currents of the i-th and j-th reference electricity meters at time t.

[0113] Continuing with the above example, the calculation results of the contact resistance differences are shown in Table 3. Among them, when the set threshold of the contact resistance difference is 2 ohms, since the maximum value of the contact resistance differences between the three reference electricity meters is 1.82 ohms and the minimum value is 0.5 ohms, both do not exceed the set threshold, it can be considered that the three reference electricity meters are operating safely without the risk of burnout; while the differences between the contact resistance values of the electricity meter of User A and the three reference electricity meters are 6.14 ohms, 7.96 ohms, and 7.64 ohms respectively, all exceeding the set threshold, it can be considered that there is a risk of burnout in the monitored metering device.

[0114]

[0115] Table 2

[0116]

[0117] Table 3

[0118] The inventors of the present disclosure have found that abnormal loop resistance or contact resistance of the metering device can lead to voltage going below or above the limit. Among them, voltage going above the limit is due to abnormal voltage caused by poor contact of the neutral line, faults in the internal voltage sampling circuit of the electricity meter, etc.; while voltage going below the limit can be caused by poor contact of the neutral line or the live wire. For the monitored electricity meter determined to have abnormal resistance (including the aforementioned abnormal loop resistance and abnormal contact resistance), the voltage data of the monitored electricity meter and the adjacent electricity meters with similar loads can be analyzed to verify whether the judgment result of abnormal resistance is correct.

[0119] In some embodiments, the monitored meter data further includes monitored meter location data. Based on the monitored meter location data and the power meter load, at least one inspection power meter is determined for the monitored meter; the average voltage of the target phase sequence within a set time range is calculated for the monitored power meter to obtain a first average voltage; the average voltage of the target phase sequence within a set time range is calculated for the inspection power meter to obtain a second average voltage; wherein, after determining whether there is a potential risk of burnout of the metering device according to the first phase sequence data, it further includes: verifying the judgment result based on the first average voltage and the second average voltage.

[0120] It can be understood that, based on the monitored meter location data and the load of the monitored meter, an inspection power meter adjacent to the location of the monitored power meter and with a load close to it can be determined for the monitored power meter. The method for determining the inspection power meter is the same as the method for determining the reference power meter, which will not be elaborated here.

[0121] Exemplarily, for the aforementioned User A, after determining that there is a potential risk of burnout of its metering device, as shown in Table 4, 3 inspection power meters are found for it (corresponding to adjacent users 5, 6, and 7 respectively). Using the voltage data of User A's power meter and the inspection power meters for 7 days, the average voltage of User A's power meter within 7 days is calculated to obtain a first average voltage, and the average voltage of the inspection power meters within 7 days is calculated to obtain a second average voltage. Then, the difference between the first average voltage and the second average voltage is calculated. When the difference is greater than the set average voltage threshold (in this example, when the set threshold is 10V), it can be considered that the result of judging that there is an abnormality in the resistance of the monitored power meter in the target phase sequence is correct, and further, the result of judging that there is a potential risk of burnout of the monitored power meter due to the resistance abnormality is also correct.

[0122] Serial number Analysis object Asset number of electricity meter Average voltage Voltage difference from adjacent users 1 Faulty user xx3368 223.6 / 2 Adjacent user 5 xx9724 234.6 11.0 3 Adjacent user 6 xx1961 234.2 10.6 4 Adjacent user 7 xx3017 234.5 10.9

[0123] Table 4

[0124] In particular, in the absence of a load, the electricity meter with poor wire contact has a relatively high contact resistance value, resulting in a low measured access voltage value. Based on this, for the monitored electricity meter determined to have abnormal resistance, the electricity meter adjacent in position under the same power supply substation can be directly selected as the verification electricity meter, and according to the voltage data measured by the monitored electricity meter and the verification electricity meter during the no-load period, verify whether the judgment result of abnormal resistance is correct. Therefore, in some other embodiments, the monitored meter data further includes the monitored meter position data, and according to the monitored meter position data, at least one verification electricity meter is determined for the monitored meter; calculate the average voltage of the target phase sequence during the set-length no-load period for the verification electricity meter to obtain a third average voltage; calculate the average voltage of the target phase sequence during the set-length no-load period for the monitored electricity meter to obtain a fourth average voltage; wherein, after judging whether there is a burning hazard of the metering device according to the first phase sequence data, it further includes: verifying the judgment result according to the third average voltage and the fourth average voltage.

[0125] In some embodiments, in response to the first average voltage being greater than the first voltage setting value, calculate the phase angles of the three-phase voltages of the monitored electricity meter; in response to the phase angles of the three-phase voltages drifting, confirm that there is a burning hazard in the neutral line of the monitored electricity meter.

[0126] The neutral line burning fault will cause neutral point drift for the three-phase four-wire meter. Figure 7 Exemplarily shows an interpretation diagram of neutral point drift, as Figure 7 shown:

[0127] When there is no neutral point drift, the potentials of the three phases A, B, and C (represented by U, V, and W respectively in the figure) relative to the potential of the neutral point (represented by N in the figure) are 220V, that is, U un =U vn =U wn =220V; at this time, the phase angles between phases A and B, between phases B and C, and between phases A and C are ∠UNV, ∠VNW, and ∠WNV respectively, and ∠UNV = ∠VNW = ∠WNU = 120°.

[0128] When the neutral point drifts, the potential of the neutral point can drift from point N to point OX via point O1, where respectively represent the voltage drift angles of phases A and B. When the potential of the neutral point is OX, the input voltages of the voltage circuit of the electricity meter are U uox 、U vox 、U wox ,where, U uox =U vox =190.53V, while Uwox = 220V + 110V = 330V. The phase angles between phases A and B, between phases B and C, and between phases A and C are ∠UOXV, ∠VOXW, and ∠WOXU respectively. Obviously, the three angles no longer satisfy being equal to 120° at this time.

[0129] Figure 7 Taking the example of N drifting in the W→N direction to explain the phenomenon of neutral point drift, it can be understood that point N can also drift in the V→N and U→N directions. Moreover, once neutral point drift occurs, the three phase angles between the three-phase voltages of A, B, and C are not all 120°, and the voltage of a certain phase in the three phases will exceed 220V, that is, voltage over-limit occurs. In some embodiments, for the three phase angles between the three-phase voltages of the monitored watt-hour meter, when at least one of the phase angles is not equal to 120°, it is considered that the phase angles of the three-phase voltages drift.

[0130] Continue Figure 7 The above-mentioned example of neutral point drift Figure 8 shows a schematic diagram of the drift of the phase sequence voltage and current angle caused by neutral point drift in this example. As Figure 8 shown, taking phase A as an example, when there is no neutral point drift, the voltage of phase A is represented by vector , and the current is represented by vector . At this time, the phase sequence voltage and current angle of phase A After neutral point drift, the voltage of phase A is represented by vector , and relative to the drift angle represents the voltage drift angle of phase A. At this time, the phase sequence voltage and current angle of phase A is Correspondingly, the voltage drift angle of phase B is The phase sequence voltage and current angle of phase B changes from ∠U VN OI VN to ∠U VO OI V . That is to say, after neutral point drift occurs, the voltage and current angle of a single phase sequence will drift. Therefore, in some embodiments, when the voltage and current angle of any phase sequence in the three phases drifts, it is determined that neutral point drift occurs, and thus there is a potential risk of burnout of the neutral line of the monitored watt-hour meter. Table 5 shows the relationship between the voltage drift angles of phases A and B and the three-phase voltage values after neutral point drift in this example.

[0131]

[0132]

[0133] Table 5

[0134] In some embodiments, in response to the first average voltage being greater than the first voltage set value, the resistance value of the voltage-dividing resistor in the voltage sampling circuit of the monitored electricity meter is obtained; in response to the resistance value of the voltage-dividing resistor being less than the resistance set value, it is determined that there is a potential risk of burnout of the terminal block of the monitored electricity meter.

[0135] Figure 9 Exemplarily, the voltage sampling circuit diagram of the electricity meter metering chip is shown. When the voltage exceeds the upper limit caused by a fault in the voltage sampling circuit of the electricity meter, due to the burnout of the terminal block, carbonized impurities and the like adhere to the resistors R201 - R206 in the voltage sampling circuit, resulting in a decrease in the resistance value of the voltage-dividing resistor. Therefore, the divided voltage V1+ input to the metering chip becomes higher, and thus the voltage value obtained by the metering chip is on the high side.

[0136] Since poor contact of the live wire or neutral wire will cause an increase in the contact resistance, resulting in the voltage falling below the lower limit. In some embodiments, in response to the first average voltage being less than the second voltage set value, it is determined that there is a potential risk of burnout of the neutral wire and / or the live wire of the target phase sequence of the monitored electricity meter.

[0137] To ensure that the obtained electricity meter data corresponds to the electricity meter of the target low-voltage user, in some embodiments, the obtained original electricity meter data is also screened according to the wiring method of the electricity meter. Specifically, it can be: screening the electricity meters with the wiring methods of three-phase four-wire and single-phase as the electricity meters of the target low-voltage users. In other embodiments, the user electricity meters with incorrect calibration files are verified according to the obtained electricity meter data. For example, if the data obtained for an electricity meter shows that the wiring method of the electricity meter is three-phase four-wire, while the calibration file of the electricity meter records a single-phase wiring, the incorrect calibration file needs to be corrected at this time.

[0138] Figure 10 A block diagram showing the hardware configuration of device 100 for implementing the method for monitoring potential risks of burnout of a metering device according to the embodiments of the present disclosure is shown. As Figure 10 shown, device 100 may include a processor 101 and a memory 102. The processor is configured to execute program instructions, and the memory is configured to store the program instructions. When the program instructions are loaded and executed by the processor, the device is caused to execute the method for monitoring potential risks of burnout of a metering device according to any one of the above embodiments. In Figure 10 device 100 shown, only the constituent elements related to this embodiment are shown. Therefore, it is obvious to those of ordinary skill in the art that: device 100 may further include components related to Figure 10Common constituent elements with different constituent elements shown therein. The specific functions implemented by the memory 102 and the processor 101 of the device 100 provided in the embodiments of this specification can be explained in contrast to the foregoing embodiments in this specification, and can achieve the technical effects of the foregoing embodiments, which will not be elaborated here.

[0139] The device 100 may correspond to a computing device having various processing functions. For example, the device 100 may be implemented as various types of devices, such as a personal computer (PC), a server device, a mobile device, etc.

[0140] The processor 101 may control the operation of the device 100. For example, the processor 101 controls the operation of the device 100 by executing a program stored in the memory 102 on the device 100. The processor 101 may be implemented by a central processing unit (CPU), a graphics processing unit (GPU), an application processor (AP), an artificial intelligence processor chip (IPU), etc. provided in the device 100. However, this disclosure is not limited thereto. In this embodiment, the processor 101 may be implemented in any suitable manner. For example, the processor 101 may take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and a form embedded with a microcontroller, etc.

[0141] The memory 102 can be hardware for storing various data and instructions processed in the device 100. For example, the memory 102 can store the processed data and the data to be processed in the device 100. The memory 102 can store data sets that have been processed or are to be processed by the processor 101, such as, for example, electricity meter data to be processed. In addition, the memory 102 can store applications, drivers, etc. to be driven by the device 100. For example, the memory 102 can store various programs related to methods for monitoring potential hazards of burned-out metering devices to be executed by the processor 101. The memory 102 can be DRAM, but the present disclosure is not limited thereto. The memory 102 can include at least one of volatile memory or non-volatile memory. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, phase change RAM (PRAM), magnetic RAM (MRAM), resistive RAM (RRAM), ferroelectric RAM (FRAM), etc. The volatile memory can include dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), PRAM, MRAM, RRAM, ferroelectric RAM (FeRAM), etc. In an embodiment, the memory 102 can include at least one of a hard disk drive (HDD), a solid state drive (SSD), a high density flash (CF) card, a secure digital (SD) card, a micro secure digital (Micro-SD) card, a mini secure digital (Mini-SD) card, an extreme digital (xD) card, caches, or a memory stick.

[0142] The specific functions implemented by the memory 102 and the processor 101 of the device 100 provided in the embodiments of this specification can be explained in contrast to the foregoing embodiments in this specification and can achieve the technical effects of the foregoing embodiments, and thus will not be elaborated here.

[0143] In summary, this disclosure takes the business analysis algorithm as the core driving force, based on the core theory of electrical basic principles, Ohm's law, and integrates algorithms such as big data correlation analysis, data distribution analysis, and clustering analysis. It proposes a method that does not rely on the high-speed power line carrier (HPLC) phase recognition function of the electricity meter itself. By accurately capturing the changes in the voltage data of the main meter and users in the transformer area, using the Pearson correlation coefficient algorithm, and taking the phase of the main meter in the transformer area as a solid benchmark, it realizes the accurate recognition of the true phase of users. On this basis, on the one hand, using the collected data of one electricity meter, extracting the time points with close loads, relying on Ohm's law, a cornerstone of physics, accurately calculating the loop resistance at the selected time points, and judging whether there are potential safety hazards in the user's operating metering equipment through the discreteness of the loop resistance. On the other hand, this disclosure further accurately locks the adjacent electricity users under the same phase according to the latitude information in the GIS geographic information system and the relationship between the box and the meter, and extracts the time points with similar loads for adjacent electricity meters. Subsequently, relying on Ohm's law, a cornerstone of physics, accurately calculating the difference between the loop resistance and the contact resistance of adjacent users' electricity meters, and judging whether there are potential safety hazards in the user's operating metering equipment through the comparison and analysis of the contact resistance difference. In addition, this disclosure comprehensively applies the univariate outlier detection method based on the Grubbs hypothesis and the LOF multivariate outlier detection method to realize the comprehensive outlier detection of voltage, current, and power data, eliminate data interference, improve data quality, and provide data guarantee for the effective recognition of the model.

[0144] The method proposed in this disclosure divides the operating state of low-voltage user metering equipment into three diagnostic results: poor neutral contact, poor live wire contact, and suspected equipment burnout fault, opening a new innovative method for the monitoring and analysis of potential safety hazards in the operation of low-voltage user metering equipment; thus promoting the transformation from the traditional "post-treatment" mode to the advanced management mode of "in-process warning and monitoring", realizing the early discovery, early prevention, and early treatment of metering equipment failures, thereby significantly reducing the user power outage time caused by equipment failures, significantly improving the user's electricity consumption satisfaction and sense of security, and laying a solid foundation for building a safer, more stable, and efficient power supply environment.

[0145] It should be noted that, for the purpose of brevity, this disclosure presents some methods and their embodiments as a series of actions and combinations thereof. However, those skilled in the art can understand that the solutions of this disclosure are not limited by the order of the described actions. Therefore, based on the disclosure or teachings of this disclosure, those skilled in the art can understand that some of the steps can be executed in other orders or simultaneously. Further, those skilled in the art can understand that the embodiments described in this disclosure can be regarded as alternative embodiments, that is, the actions or modules involved therein are not necessarily required for the implementation of certain solutions of this disclosure. Additionally, according to different solutions, this disclosure focuses on the descriptions of some embodiments. In view of this, those skilled in the art can understand that for the parts not detailed in a certain embodiment of this disclosure, they can also refer to the relevant descriptions of other embodiments.

Claims

1. A method for monitoring potential burnout hazards of metering devices, comprising: Identifying the monitored electric energy meter corresponding to the metering device to be monitored; Obtaining data of the monitored electric energy meter to obtain monitored meter data, where the monitored meter data includes monitored meter voltage data and monitored meter current data; Identifying the phase sequence of the monitored meter voltage data and / or the monitored meter current data to obtain a phase sequence identification result; According to the phase sequence identification result, extracting data corresponding to the target phase sequence from the monitored meter data to obtain first-phase sequence data; Judging whether there are potential burnout hazards for the metering device according to the first-phase sequence data; Among them, judging whether there are potential burnout hazards for the metering device according to the first-phase sequence data includes: Extracting data from the first-phase sequence data according to the load of the monitored electric energy meter to obtain second-phase sequence data; Calculating the loop resistance of the monitored electric energy meter in the target phase sequence according to the second-phase sequence data; Judging whether there are potential burnout hazards for the metering device according to the discreteness of the loop resistance; Calculating the discreteness of the loop resistance according to the following formula: Wherein, Rs represents the loop resistance; n represents the number of time points included in the second phase sequence data; μ represents the mean value of Rs; When S is greater than the discreteness threshold, it is determined that there are potential burnout hazards for the metering device; According to the formula E = {D1 t |I t *K ≥ 1}, the first-phase sequence valid data is screened from the first-phase sequence data, where D1 represents the first-phase sequence data, E represents the first-phase sequence valid data, I t represents the absolute value of the current magnitude at time t in D1, with the unit of ampere (A), and K is the comprehensive multiplier of the watt-hour meter; According to the formula Select the second phase sequence data from the first phase sequence valid data, where Is represents the current data in the second phase sequence data, |Is| represents the absolute value of the current data in the second phase sequence data, and max(x), min(x) represent the maximum value and minimum value of x respectively.

2. The method according to claim 1, wherein the monitored meter data further includes monitored meter position data; Determining at least one inspection electric energy meter for the monitored electric energy meter according to the monitored meter position data and the load of the electric energy meter; Calculating the average voltage of the target phase sequence of the monitored electric energy meter within a set time range to obtain a first average voltage; Calculating the average voltage of the target phase sequence of the inspection electric energy meter within a set time range to obtain a second average voltage; wherein, after judging whether there are potential burnout hazards for the metering device according to the first-phase sequence data, it further includes: Verifying the judgment result according to the first average voltage and the second average voltage.

3. The method according to claim 2, further comprising: In response to the first average voltage being greater than the first voltage setting value, Calculating the phase angles of the three-phase voltages of the monitored electric energy meter; In response to the phase angles of the three-phase voltages drifting, Confirming that there are potential burnout hazards for the neutral line of the monitored electric energy meter.

4. The method according to claim 2, further comprising: In response to the first average voltage being greater than the first voltage setting value, Obtaining the resistance value of the voltage-dividing resistor in the voltage sampling circuit of the monitored electric energy meter; In response to the resistance value of the voltage-dividing resistor being less than the resistance setting value, Determining that there are potential burnout hazards for the terminal block of the monitored electric energy meter.

5. The method according to claim 2, further comprising: In response to the first average voltage being less than the second voltage setting value, Determining that there are potential burnout hazards for the neutral line and / or the live wire of the target phase sequence of the monitored electric energy meter.

6. The method according to claim 1, wherein Obtaining data of the monitored electric energy meter to obtain monitored meter data, including: Obtaining the original data of the monitored electric energy meter; Performing outlier detection and / or missing value imputation on the original data to obtain the monitored meter data.

7. A device for monitoring potential burnout hazards of metering devices, comprising: A processor configured to execute program instructions; And A memory configured to store the program instructions, which, when loaded and executed by the processor, cause the device to perform the method according to any one of claims 1-6.

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