Method and device for monitoring burning loss hidden danger of metering equipment

Abstract

The invention discloses a method and device for monitoring a burning loss hidden danger of metering equipment, and relates to the technical field of power systems. The method comprises the following steps: confirming a monitored electric energy meter corresponding to monitored metering equipment; acquiring data of a monitored electric energy meter to obtain monitored meter data, the monitored meter data including monitored meter voltage data and monitored meter current data; identifying the phase sequence of the voltage data of the monitored meter and / or the current data of the monitored meter to obtain a phase sequence identification result; according to the phase sequence identification result, extracting data corresponding to a target phase sequence from the monitored table data to obtain first phase sequence data; and according to the first phase sequence data, judging whether the metering equipment has a burning loss hidden danger or not. According to the method, the burning loss hidden danger of the metering equipment can be accurately identified, and the occurrence position of the burning loss hidden danger can be positioned.

Description

[0001] This application is a divisional application of the patent with application number 202411323732.4. The application date of the original application is September 23, 2024. The name of the invention is: A method and device for monitoring the hidden dangers of burning of metering equipment. Technical Field

[0002] The present disclosure generally relates to the field of power system technology. More specifically, the present disclosure relates to a method and apparatus for monitoring potential burnout hazards in metering equipment. Background Art

[0003] Metering equipment, the core link between power supply and user consumption, is invaluable for ensuring the continuity and reliability of power supply. Therefore, deepening electricity safety management and proactively addressing and effectively curbing metering equipment burnout and other potential hazards have become urgent priorities.

[0004] In the existing technology, 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 every day is extremely large, which not only leads to a heavy workload for maintenance personnel, but also causes the consumption of valuable 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 the occurrence of major safety accidents (such as fires) has become a major issue and challenge that the industry urgently needs to solve.

[0005] The rapid adoption of IoT meters and version 2.0 smart meters, along with the continuous improvement in the informatization and automation of the next-generation Marketing 2.0 system and the Electricity Consumption Information Collection 2.0 system, has provided strong data support and technical foundation for intelligent monitoring and analysis of safety hazards in metering equipment operations. In particular, the comprehensive upgrade of the Electricity Consumption Information Collection 2.0 system has significantly improved the density, frequency, completeness, and timeliness of load curve data collected from low-voltage electricity meters, laying a solid foundation for big data application and analysis. This system has demonstrated strong monitoring and early warning capabilities in areas such as power outage management, electricity theft detection, and intelligent operations and maintenance. By collecting operational data from electricity meters, such as voltage, current, and power, and integrating it with operational experience, it can reliably analyze overloads and voltage anomalies, providing early warning of potential overheating or burnout risks. However, the domestic industry still lacks efficient and accurate analytical methods and technical means to identify metering equipment burnout hazards using multi-dimensional data features and deep correlation analysis, and the potential in this area has yet to be fully realized and tapped.

[0006] In view of this, there is an urgent need to provide a method for monitoring the hidden dangers of burning of measuring equipment, so as to effectively utilize the large amount of data collected by the measuring equipment, accurately identify the hidden dangers of burning of the measuring equipment, and thus prevent the occurrence of burning accidents of the measuring equipment. Summary of the Invention

[0007] In order to at least solve the problems described in the above background technology section, the present disclosure proposes the following technical solutions and multiple embodiments thereof.

[0008] In the first aspect, the present disclosure proposes a method for monitoring the hidden dangers of burning of metering equipment, including: confirming the monitored electric energy meter corresponding to the monitored metering equipment; obtaining data of the monitored electric energy meter to obtain monitored meter data, the monitored meter data including 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; based on the phase sequence identification result, extracting data corresponding to the target phase sequence from the monitored meter data to obtain first phase sequence data; based on the first phase sequence data, judging whether the metering equipment has a hidden danger of burning.

[0009] In a second aspect, the present disclosure proposes a device for monitoring the hidden dangers of burning of metering equipment, comprising: a processor configured to execute program instructions; and a memory configured to store the program instructions, so that when the program instructions are loaded and executed by the processor, the device executes the method described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0011] Figure 1 An exemplary schematic diagram illustrating the connection relationship of metering devices in a power supply area in some embodiments of the present disclosure is shown.

[0012] Figure 2 An exemplary flow chart of a method for monitoring burning hazards of metering equipment in some embodiments of the present disclosure is shown.

[0013] Figure 3 This is a conceptual diagram explaining the reachable distance.

[0014] Figure 4 An exemplary flow chart of a method for determining whether a metering device has a potential burning risk based on first phase sequence data in some embodiments of the present disclosure is shown.

[0015] Figure 5 An exemplary flow chart of a method for determining whether a metering device has a potential burning risk based on first phase sequence data in some other embodiments of the present disclosure is shown.

[0016] Figure 6 An exemplary flow chart of a method for determining whether a metering device has a potential burning risk based on first phase sequence data and phase sequence data in a reference table in some embodiments of the present disclosure is shown.

[0017] Figure 7 A neutral point drift interpretation diagram is shown as an example.

[0018] Figure 8 The diagram exemplarily shows that the neutral point drift causes the phase sequence voltage and current angle drift.

[0019] Figure 9 The voltage sampling circuit diagram of the electric energy meter measurement chip is shown as an example.

[0020] Figure 10 The block diagram shows the hardware configuration of the apparatus 100 that can implement the method for monitoring the hidden danger of burning of metering equipment according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of this disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this disclosure, not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this disclosure.

[0022] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0023] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0024] As used in this specification and claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0025] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0026] In the power system, metering equipment is a series of devices used to measure and record electricity usage, including key components such as electricity meters, transformers, junction boxes, knife switches and wires.

[0027] Figure 1 The following is an exemplary schematic diagram showing the connection relationship of metering equipment in the power supply area in some embodiments of the present disclosure. Figure 1 As shown, it can be a three-phase power supply of a three-phase generator or transformer, which outputs stable and efficient three-phase electricity; three-phase electricity consists of three alternating currents with the same frequency, equal amplitude, and phase difference of 120 degrees, and is transmitted by live wires, corresponding to Figure 1The A phase live wire, B phase live wire, and C phase live wire, the live wire can also be called the phase wire; the zero wire, also known as the neutral wire, has an ideal potential of zero and is used to form a power supply circuit with the live wire.

[0028] like Figure 1 The power supply system shown in FIG includes a total meter for the area and a user energy meter. The total meter for the area is used to measure the power consumption of the entire area, while the user energy meter (such as Figure 1 The single-phase energy meter A and three-phase energy meters B and C in the diagram are used to measure the amount of energy consumed by each user or a subset of users. In this power supply system, single-phase power can be drawn from any phase and neutral wire to power single-phase loads. For three-phase loads, three-phase power can be drawn from all three phases and the neutral wire to power three-phase loads. Depending on the type of load, user energy meters can be divided into single-phase and three-phase meters.

[0029] continue Figure 1 In some embodiments, knife switches S1, S2 and a single-phase electric energy meter A may be provided on a single-phase power supply circuit consisting of any phase line and a neutral line. In other embodiments, knife switches S3, S4 and a three-phase electric energy meter B may be provided on a three-phase power supply circuit consisting of three phase lines and a neutral line. The knife switch is used to control the on and off of the circuit. During equipment maintenance, troubleshooting or emergency situations, the power supply can be quickly cut off to ensure the safety of personnel and equipment. In some embodiments, a joint junction box may be provided for both a single-phase power supply circuit and a three-phase power supply circuit. The joint junction box is used to reliably connect the electric energy meter, the mutual inductor and the power line together to ensure the accurate transmission of the power signal and prevent the influence of external factors on the metering equipment. In some embodiments, a mutual inductor may be provided in the three-phase power supply circuit. The mutual inductor is divided into a voltage mutual inductor and a current mutual inductor, which is used to convert high voltage and large current into low voltage and small current to facilitate the safe operation of the measurement and protection equipment.

[0030] In a power supply area, a large number of low-voltage users can be supplied with electricity. For example, all residents in a residential area share one area, and all offices in an office building share one area. Each low-voltage user has a set of specific metering equipment to ensure accurate measurement of the power consumption of all users. Figure 1 The power supply system shown can be equipped with a specified number of metering devices according to actual needs and the number of low-voltage users.

[0031] As the usage time increases, the various components of the metering equipment gradually age and the insulation performance deteriorates, making it easy to have problems such as leakage and short circuit, which can lead to burning failures. The hidden dangers of burning of the metering equipment can be monitored based on the electrical information collected by the electric energy meter in the metering equipment. The inventors of the present disclosure found that: in the existing user electric energy meters, the phase sequence of the data collected by the single-phase electric energy meter defaults to the A phase of the district master meter; the data collected by the three-phase electric energy meter defaults to the A, B, and C phases of the district master meter; however, in reality, the phase sequence of the data collected in the user's electric energy meter and the phase sequence of the district master meter do not necessarily conform to the default value. This mismatch between the phase sequence of the collected data and the actual physical phase line makes it impossible to effectively determine which part of the power supply phase line of the metering equipment has a fault, and it is also impossible to use the collected data of multiple electric energy meters to monitor the hidden dangers of burning of the metering equipment. For example, for 10 three-phase electricity meters under a power supply substation, if the correspondence between the phase sequence of the three-phase collected data of each electricity meter and the three phases A, B, and C of the substation total meter is unclear, the phase sequence correspondence between the collected data of these 10 three-phase electricity meters is also unclear, making it impossible to align the three-phase data collected by these electricity meters, and even more impossible to perform in-depth data analysis.

[0032] In view of this, the present disclosure provides a method for monitoring the hidden dangers of burning of metering equipment, so as to effectively utilize the big data collected by smart electricity meters during operation and accurately identify the hidden dangers of burning of metering equipment.

[0033] Figure 2 An exemplary flow chart of a method for monitoring the potential burnout of metering equipment in some embodiments of the present disclosure is shown. Figure 2 As shown, the method includes: step 201, confirming the monitored electric energy meter corresponding to the monitored metering device. Step 202, acquiring the data of the monitored electric energy meter to obtain the monitored meter data, wherein the monitored meter data includes the monitored meter voltage data and the monitored meter current data. Step 203, identifying 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, based on the phase sequence identification result, extracting the data corresponding to the target phase sequence from the monitored meter data to obtain the first phase sequence data. Step 205, judging whether the metering device has a burning risk based on the first phase sequence data.

[0034] Regarding step 201, it is understood that a distribution system supplying power to multiple low-voltage users contains a large number of metering devices, each of which has a corresponding energy meter. The monitored metering device is the metering device for which a burnout risk assessment is required. Accordingly, the energy meter corresponding to the monitored metering device is the monitored energy meter.

[0035] For step 202, it can be understood that the monitored meter data may include operating data. Operating data is the time series data generated by the electric energy meter during actual operation, including active power data, voltage data, current data, electricity data, substation line loss rate data, etc. The method of obtaining operating data may be to obtain various operating data once at a fixed time interval. For example, real-time data such as active power, current, and voltage may be obtained at the same time at every hour of the 24 hours of each day, thereby obtaining various operating data at 24 time points every day; in this case, active power, current, voltage, electricity, and substation line loss rate data can constitute multivariate time series data. In addition, the obtained operating data includes historical operating data of the monitored electric energy meter in the past week, the past three months, or the past year.

[0036] Continuing with step 202, in some embodiments, the data of the monitored electricity meter may also include: archival data in the new generation electricity consumption information collection 2.0 system, marketing 2.0 system, user GIS coordinate data, and user electricity meter box-meter relationship data; wherein, the archival data mainly includes static information about the attributes and installation and use of the electricity meter itself, such as the substation capacity, comprehensive ratio, substation type, user information, metering point information, and electricity meter information; the electricity meter information may include the electricity meter model, manufacturer, installation time, and asset number.

[0037] Regarding step 203, it can be understood that the input voltage of the single-phase electric energy meter is provided by any phase line in the substation. Identifying the phase sequence of the single-phase electric energy meter to obtain the phase sequence identification result is to determine whether the data collected by the electric energy meter corresponds to phase A, phase B, or phase C in the three-phase electricity of the substation. The input voltage of the three-phase electric energy meter is provided by three phase lines. Identifying the phase sequence of the three-phase electric energy meter to obtain the phase sequence identification result is to determine the correspondence between the three-phase data collected by the electric energy meter and phase A, phase B, and phase C in the three-phase electricity of the substation. The voltage and current data of the monitored electric energy meter both carry phase sequence information. Therefore, the phase sequence identification result can be obtained based on the voltage data or the current data, or based on the voltage data and the current data.

[0038] Regarding step 204, it is understood that since a single-phase energy meter has only one phase, its first phase sequence data is the acquired monitored meter data itself. For a three-phase energy meter, however, its energy meter data includes data corresponding to each of the three phases. The target phase sequence can be any one, two, or three 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 energy meter data. In this case, the first phase sequence data can be data corresponding to at least one of the three phases.

[0039] Therefore, the method for monitoring metering equipment burnout hazards provided by this disclosure can extract the phase sequence data of the monitored meter, which corresponds to the phase sequence of the substation's main meter, by identifying the phase sequence of the meter being monitored. Because the identification of burnout hazards is performed based on the phase sequence data of the monitored meter, it can be determined whether there is a burnout hazard in the power supply circuit corresponding to the target phase sequence. This not only accurately identifies the metering equipment burnout hazard, but also helps locate the location of the burnout hazard.

[0040] In some embodiments, identifying the phase sequence of data collected from 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, it corresponds to phase A, phase B or phase C in the three-phase electricity of the substation main meter.

[0041]

[0042] In formula (1), z i It represents the voltage or current data of a phase in the total table of the substation. It can be understood that when x is the voltage data, z i That is, voltage data; when x is current data, z i That is the current data. Cov(z i ,x) represents z i Calculate the covariance with x, Var(z i ), Var(x) respectively represent the i 、x calculate the variance. Then It represents the correlation coefficient between the voltage or current data of a certain phase of the substation master meter 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 substation master meter corresponding to the single-phase collected data of the user's electric energy meter.

[0043] For example, the method for calculating the correlation coefficient between the A-phase voltage data in the user's electric energy meter collected data and the phase voltage data of the substation total meter can be:

[0044]

[0045] Among them, P Uza,Uha represents the correlation coefficient between the phase A voltage Uza of the total meter in the substation and the phase A voltage Uha of the user; P Uzb,Uha represents the correlation coefficient between the phase B voltage Uzb of the total meter in the substation and the phase A voltage Uha of the user; Uzc,UhaRepresents the correlation coefficient between the phase C voltage Uzc of the total meter in 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 in 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 in 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 in 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.

[0046] The phase sequence identification of phases B and 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 master meter respectively. However, using the voltage data of the electric energy meter in the substation for 7 days, the correlation coefficients of the A, B, and C three-phase collected data of EEM and the A, B, and C three-phase collected data of the substation master 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 total table, the phase with the highest correlation coefficient with EEM's phase B is phase C in the substation total table, and the phase with the highest correlation coefficient with EEM's phase C is phase A in the substation total 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 total table, the collected data of EEM's phase B corresponds to phase C in the substation total table, and the collected data of EEM's phase C corresponds to phase A in the substation total table.

[0047]

[0048] Table 1

[0049] In some embodiments, obtaining the data of the monitored electric energy meter to obtain the monitored meter data includes: obtaining the original data of the monitored electric energy meter; performing outlier detection and / or missing value interpolation on the original data to obtain the monitored meter data.

[0050] It is understood that the raw data described is data directly obtained from the electricity meter, generated by the electricity meter during operation. However, due to communication issues between the terminal and the meter, abnormal data may exist in the raw data. Before using electricity meter data to identify potential burnout risks of metering equipment, these abnormal values ​​must be processed.

[0051] In some embodiments, a univariate outlier detection method based on the Grubbs' hypothesis test is used to detect and remove outliers from the univariate time series in the raw data. It is 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, voltage, or power of a particular phase.

[0052] Grubbs' Test is a hypothesis testing method often used to detect a single outlier in a univariate data set that follows a normal distribution. If an outlier is present, it must be the maximum or minimum value in the data set. The null hypothesis and alternative hypothesis are as follows: H0: There are no outliers in the data set; H1: There is one outlier in the data set. The algorithm flow for using Grubbs' Test to detect anomalies in a univariate time series x is as follows:

[0053] 1. Find the mean of a univariate time series Standard deviation s, minimum value min and maximum value max;

[0054] 2. Calculate the difference between min, max and mean respectively. The value with the larger difference (min or max) is suspicious.

[0055] 3. Find the standard score of suspicious values x_s is a suspicious value. If z_score is greater than the Grubbs threshold, then the suspicious value is an outlier. The Grubbs threshold is obtained by looking up the table.

[0056] In some embodiments, a method based on a 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 a single-phase or three-phase electricity meter, the multivariate is composed of variables such as single-phase current, voltage, and power. LOF is based on density analysis and detects outliers through local data density. The LOF method mainly determines whether the point p is an outlier by comparing the density of each point (p) with its neighboring points. The lower the density of point p, the higher the probability that point p is an outlier. In the LOF method, the density is calculated by 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 the density through the k-neighborhood of the point. Since the density is not calculated based on all points globally, it is called a "local outlier factor."

[0057] The LOF algorithm calculates an outlier factor (LOF) for each point in the dataset and determines whether it is an outlier by judging whether the LOF is close to 1. If the LOF is much greater than 1, it is considered an outlier; if the LOF is close to 1, it is considered a normal point. The following introduces the important concepts related to the calculation of the outlier factor in the LOF algorithm:

[0058] ① K distance of point p: Sort the distances between other points and point p from small to large. The distance to the kth point closest to point p is the k distance of point p.

[0059] ②The k-th distance neighborhood of point p: the set of points whose distance to point p is less than or equal to k distance.

[0060] ③ The reachable distance of point p relative to point o: the kth reachable distance from point p to point o = max(k nearest neighbor distance of point o k_distance(o), distance from point p to point o ‖po‖).

[0061] Figure 3 This is an exemplary concept diagram of reachable distance, such as Figure 3 As shown in , for different points, the kth reachable distance to point o is calculated differently. Figure 3 In the example, the kth reachable distance from point p1 to point o is reach-dist k (p1,o), the dotted line is the k-nearest neighbor distance of point o (that is, the distance from the kth nearest point to point o, where k is 3). 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); if the distance from point p2 to point o ‖p2-o‖ is greater than k-distance(o), then the kth reachable distance from point p2 to point o is reach-dist k (p2,o) is the distance from point p2 to point o ‖p2-o‖.

[0062] 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 neighbors (i.e., the reciprocal of the average of all reachable distances within the k-th distance neighborhood of point p). The larger the value, the more compact the data is.

[0063]

[0064] In formula (2), lrd k (p) is the local reachability density of point p, k is the number of nearest neighbors, reach-dist k (p,o) is the kth distance neighborhood of point p N k The reachable distance from each point o to point p in (p).

[0065] 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 the points in the neighborhood divided by the local reachability density of point p. The size of the local outlier factor (LOF) represents the credibility of the point as an outlier. That is, the larger the factor, the more likely the point is an outlier.

[0066]

[0067] In formula (3), LOF k (p) is the local outlier factor of point p, lrd k (o) is the kth distance neighborhood of point p N k The local reachability density of each point o in (p).

[0068] In the data collection of electricity consumption information collection systems, missing data is an inevitable and must-address issue. In particular, missing data such as voltage, current, and power can directly impact the accuracy of identifying and predicting abnormal clues to operational safety hazards. Therefore, addressing missing data is crucial.

[0069] In some embodiments, the missing attribute is used as the dependent variable and the other attributes are used as the independent variables. The missing value is interpolated using the n-order polynomial approximation method. The interpolation process is as follows:

[0070] The first step is to select a 3rd (or 5th) order fitting equation. Assume that the function fitting equation between time series data (such as voltage and current) x and attribute time t is:

[0071] x(t i )=a0+a1t i +a2t i 2 +a3t i 3 (4)

[0072] The second step is to select the time series data [x n , t n ] before and after N data: [x i , t i ] into the fitting equation, and solve a0, a1, a2, and a3 as the unknown coefficients of the fitting equation.

[0073] The third step is to use the least squares method to make the interpolation [x n , t n ] values ​​to a minimum, and the coefficients a0, a1, a2, and a3 are solved, that is, the mean square error equation shown in formula (5) is solved:

[0074]

[0075] The matrix calculation of coefficients a0, a1, a2, and a3 is established as shown in formula (6):

[0076]

[0077] Let formula (6) be TA=X, then the following derivation can be performed to solve the coefficients a0, a1, a2, and a3 of the fitting equation (4):

[0078]

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

[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 An exemplary flow chart of a method for determining whether a metering device has a potential burnout risk based on first phase sequence data in some embodiments of the present disclosure is shown. It can be understood that: Figure 4 This is a specific implementation of the above step 205. Figure 4 As shown, in some embodiments, determining whether the metering device has a potential burnout risk based on the first phase sequence data includes: Step 401: extracting data from the first phase sequence data based on 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 at the target phase sequence based on the second phase sequence data. Step 403: determining whether the metering device has a potential burnout risk based on the discreteness of the loop resistance.

[0082] The load of the energy meter can be measured using the current and power data of the energy meter. As mentioned above, the monitored meter data can be multivariable time series data. Therefore, for the data at each moment, the load of the energy meter at that moment can be measured based on its current or power value. Based on the load of the energy meter, a data subset with a higher load approximation is extracted from the first phase sequence data to obtain the second phase sequence data. In some embodiments, extracting a data subset with a higher load approximation from the first phase sequence data to obtain the second phase sequence data includes the following steps:

[0083] First, valid data with power load is filtered 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 The absolute value of the current at time t in D1 is expressed in amperes (A). K is the comprehensive multiplier of the energy meter. The comprehensive multiplier is the multiplier used when measuring energy and is determined by the product of the current transformer ratio and the voltage transformer ratio. According to formula (8), the data at times with low load can be filtered out from the first phase sequence data, thereby retaining the data at times with effective load, and obtaining the first phase sequence effective data (E).

[0086] Then, the first phase sequence valid data is sorted according to the absolute value of the current value, and a data subset with a high load similarity is selected to obtain the second phase sequence data (D2). The deviation rate of the absolute value of the current data as shown in formula (9) is used to measure whether the load similarity is high enough.

[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) represent the maximum and minimum values ​​of x, respectively. According to formula (9), the second phase sequence data with a higher load similarity can be extracted from the first phase sequence valid data.

[0089] Regarding 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 the same moment can be calculated according to the voltage data and current data at the same moment. Therefore, the loop resistance (Rs) is calculated for the second phase sequence data using formula (10) as the loop resistance of the monitored electric energy meter in the target phase sequence.

[0090]

[0091] The inventors of this disclosure have discovered that for metering equipment without a burning risk, the loop resistance value remains essentially stable under close load conditions. However, for metering equipment with a burning risk, even under close load conditions, there is a sudden inflection point in the loop resistance value before and after the burning risk occurs, meaning the loop resistance value fluctuates. Therefore, the presence of a burning risk in a metering equipment can be determined 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 can be determined whether the metering device has a potential 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 fluctuation, indicating that there is a potential burnout risk in the metering device circuit.

[0095] The inventors of the present disclosure discovered that: in a power supply substation, each phase of the substation main 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 substation on this phase sequence can be referred to to determine whether there is an abnormality in the phase sequence data of the monitored electricity meter, thereby determining whether there is a hidden danger of burning of the monitored metering equipment. For example: when the data of other electricity meters on the target phase sequence are relatively close, and only the data of the monitored electricity meter on the target phase sequence appears as an outlier, then there is a greater degree of confidence that the monitored electricity meter has a hidden danger of burning.

[0096] Figure 5 The following is an exemplary flow chart showing a method for determining whether a metering device has a burning risk according to the first phase sequence data in some other embodiments of the present disclosure. It can be understood that, Figure 5 This is another specific implementation of the above step 205. Figure 5 As shown, in some embodiments, determining whether the metering device has a potential burnout risk based on the first phase sequence data includes: Step 501, wherein the monitored meter data also includes monitored meter location data; and determining at least two reference electric energy meters based on the monitored meter location data and the load of the monitored meter. Step 502, obtaining data of the reference electric energy meters at the target phase sequence to obtain reference meter phase sequence data. Step 503, determining whether the metering device has a potential burnout risk based on the first phase sequence data and the reference meter phase sequence data.

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

[0098] In some embodiments, the distance between the electric energy meters is calculated based on the Haversine formula using the GIS latitude and longitude coordinate data of the meter box. The Haversine formula is a formula for calculating the great circle distance between two points on the earth. Due to its simplicity and accuracy, it is widely used in fields such as geographic information systems, navigation, and map making. 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), lat1 and lat2 are latitudes (in radians), lon1 and lon2 are longitudes, and r is the mean radius of the Earth, which is 6371 kilometers. In some embodiments, a distance threshold can be set, and other energy meters whose distance to the monitored energy meter is less than the set threshold are considered to be neighboring energy meters. The distance threshold value can be 10 meters.

[0101] The above-described embodiments determine neighboring meters for a monitored meter based on the meter box GIS longitude and latitude coordinate data. In other embodiments, neighboring meters are determined based on the meter box-meter relationship in the marketing field operation system. Within a power distribution area, a distribution box typically connects multiple meters, so meters connected to the same distribution box can be considered neighboring meters. In other embodiments, neighboring meters are determined based on a combination of meter box GIS longitude and latitude coordinate data and the meter 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 data from a reference electric energy meter to obtain reference table data; identifying the phase sequence of the reference table voltage data and / or current data to obtain a reference table phase sequence identification result; and extracting data corresponding to the target phase sequence from the reference table data based on the reference table phase sequence identification result to obtain the reference table phase sequence data. This part has been described in sufficient detail above and will not be repeated here.

[0103] Regarding step 503, it is understood that the monitored energy meter and the reference energy meter are similar in location and load. If there is no risk of burning out the metering equipment, the data from each meter at the same phase sequence should be relatively close. However, for metering equipment with a risk of burning out, its phase sequence data will exhibit outlier characteristics. Therefore, it is possible to determine whether the metering equipment has a risk of burning out based on the first phase sequence data and the second phase sequence data.

[0104] Figure 6 The following is an exemplary flow chart of a method for determining whether a metering device has a burning risk according to first phase sequence data and reference table phase sequence data in some embodiments of the present disclosure. It can be understood that, Figure 6 This is a specific implementation of the above step 503. Figure 6 As shown, in some embodiments, determining whether the metering device has a potential burnout risk 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 electric energy meter and the reference meter at a set time based on the first phase sequence data and the reference table phase sequence data to obtain a first contact resistance difference. Step 602: calculating the contact resistance difference between reference electric energy meters at a set time based on the reference table phase sequence data to obtain a second contact resistance difference. Step 603: determining whether the metering device has a potential burnout risk based on the first contact resistance difference and the second contact resistance difference.

[0105] It is understandable that the loop impedance of the user's electric energy meter is mainly composed of the user's load resistance, the contact resistance and the wire resistance between the substation master meter and the user's electric energy meter. The contact resistance reflects the contact condition of the wires in the metering equipment. High contact resistance means poor contact of the wires, which poses a risk of burning. For two adjacent electric energy meters, the wire resistance from the substation master meter to each electric energy meter is almost the same. When the user's load resistance is consistent or close, the difference in loop resistance of the same phase sequence is the difference in contact resistance. In this way, the loop resistance of the monitored electric energy meter and the reference electric energy meter at the same set time can be calculated based on the first phase sequence data and the reference table phase sequence data, respectively. Then, the loop resistance of the monitored electric energy meter is subtracted from the loop resistance of the reference electric energy meter to obtain the first contact resistance difference; the loop resistance of different reference electric energy meters is subtracted to obtain the second contact resistance difference.

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

[0107] For example, take low-voltage user A as an example, whose electricity meter number is XX3368. The collected data of phase A of user A's electricity meter on a certain day are selected for analysis, and the data described in Table 2 are obtained. As can be seen from Table 2, 21:00 is the time when the load of user A's electricity meter is the minimum, and 20:00 is the time when the load of user A's electricity meter is the maximum. However, at 20:00, there is only one user adjacent to user A's position 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 three users adjacent to user A's position 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 meter according to Ohm's law.

[0108]

[0109] in, It represents the loop resistance value of user A’s electric energy meter at time t, It represents the voltage value of user A's electric energy meter at time t, represents the current value of user A's electric energy meter at time t; k is used to represent the kth reference electric energy meter of user A's electric energy meter at time t, and the values ​​are 1, 2, 3, ...; represents the loop resistance value of the kth reference energy meter at time t, represents the voltage value of the kth reference electric energy meter at time t, Represents the current value of the kth reference electric energy meter at time t.

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

[0111]

[0112] in, It represents the contact resistance difference between user A’s electric energy meter and the kth reference electric energy meter at time t; represents the contact resistance difference between the i-th reference energy meter and the j-th reference energy meter at time t; They represent the loop resistance of the i-th and j-th reference energy meters at time t respectively; Respectively represent the voltage of the i-th and j-th reference energy meters at time t; Respectively represent the current of the i-th and j-th reference energy meters at time t.

[0113] Continuing with the above example, the calculation results of the contact resistance difference are shown in Table 3. When the contact resistance difference threshold is set to 2 ohms, since the maximum contact resistance difference between the three reference energy meters is 1.82 ohms and the minimum is 0.5 ohms, both of which do not exceed the set threshold, the three reference energy meters can be considered to be operating safely and there is no risk of burnout. However, the contact resistance differences between User A's energy meter and the three reference energy meters are 6.14 ohms, 7.96 ohms, and 7.64 ohms, respectively, all exceeding the set threshold. Therefore, it can be considered that the monitored metering equipment has a risk of burnout.

[0114]

[0115] Table 2

[0116]

[0117] Table 3

[0118] The inventors of the present disclosure have discovered that abnormal loop resistance or contact resistance of the metering equipment will cause the voltage to exceed the lower limit or the upper limit. Among them, the voltage exceeding the upper limit is caused by voltage abnormality due to poor contact of the neutral wire, failure of the voltage sampling circuit inside the electric energy meter, etc.; while the voltage exceeding the lower limit may be caused by poor contact of the neutral wire or the live wire. For the monitored electric energy meter that is judged to have abnormal resistance (including the aforementioned abnormal loop resistance and abnormal contact resistance), the voltage data of the monitored electric energy meter and its neighboring electric energy meters with similar loads can be analyzed to verify whether the resistance abnormality judgment result is correct.

[0119] In some embodiments, the monitored meter data also includes the monitored meter location data, and at least one inspection electric energy meter is determined for the monitored meter based on the monitored meter location data and the electric energy meter load; the average voltage of the target phase sequence within the set time range is calculated for the monitored electric energy meter to obtain a first average voltage; the average voltage of the target phase sequence within the set time range is calculated for the inspection electric energy meter to obtain a second average voltage; wherein, after judging whether the metering equipment has a burning risk based on the first phase sequence data, it also includes: checking the judgment result based on the first average voltage and the second average voltage.

[0120] It is understood that based on the location data of the monitored meter and the load of the monitored meter, a test meter with a location and load similar to the monitored meter can be determined for the monitored meter. The method for determining the test meter is the same as the method for determining the reference meter and will not be repeated here.

[0121] For example, for the aforementioned user A, after determining that its metering equipment has a hidden danger of burning, as shown in Table 4, three test electricity meters are found for it (corresponding to adjacent users 5, 6, and 7, respectively). Using the voltage data of user A's electricity meter and the test electricity meter for 7 days, the average voltage of user A's electricity meter within 7 days is calculated to obtain the first average voltage, and the average voltage of the test electricity meter within 7 days is calculated to obtain the second average voltage. Then the first average voltage is subtracted from the second average voltage. 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 the resistance of the monitored electricity meter at the target phase sequence is abnormal is correct, and then the result of judging that the monitored electricity meter has a hidden danger of burning based on the resistance abnormality is also correct.

[0122] Serial number Analysis Object Energy meter asset number Average voltage Pressure difference with adjacent users 1 Faulty user xx3368 223.6 / 2 Neighboring user 5 xx9724 234.6 11.0 3 Neighboring user 6 xx1961 234.2 10.6 4 Neighboring user 7 xx3017 234.5 10.9

[0123] Table 4

[0124] In particular, in the absence of load, the electric energy meter with poor wire contact has a high contact resistance value, which results in a low measured access voltage value. Based on this, for the monitored electric energy meter that is judged to have abnormal resistance, the electric energy meter located adjacent to the electric energy meter in the same power supply area can be directly selected as the verification electric energy meter, and the voltage data measured by the monitored electric energy meter and the verification electric energy meter during the no-load period can be used to verify whether the judgment result of the resistance abnormality is correct. Therefore, in some other embodiments, the monitored meter data also includes the monitored meter position data, and at least one verification electric energy meter is determined for the monitored meter based on the monitored meter position data; the average voltage of the target phase sequence in the no-load period of a set length is calculated for the verification electric energy meter to obtain a third average voltage; the average voltage of the target phase sequence in the no-load period of a set length is calculated for the monitored electric energy meter to obtain a fourth average voltage; wherein, after judging whether the metering equipment has a burning risk based on the first phase sequence data, it also includes: verifying the judgment result based on 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, the phase angle of the three-phase voltage of the monitored electric energy meter is calculated; in response to the phase angle of the three-phase voltage drifting, it is confirmed that the neutral line of the monitored electric energy meter has a risk of burning out.

[0126] A neutral wire burnout fault will cause the neutral point to drift in a three-phase four-wire meter. Figure 7 The neutral point drift interpretation diagram is shown as an example. Figure 7 As shown:

[0127] When there is no neutral point drift, the potential of the three phases A, B, and C (represented by U, V, and W in the figure) relative to the potential of the neutral point (represented by N in the figure) is 220V, that is, U un =U vn =U wn =220V; at this time, the phase angle between phases A and B, the phase angle between phases B and C, and the phase angle between phases A and C are ∠UNV, ∠VNW, and ∠WNU 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 O1 and finally to point OX. Respectively represent the voltage drift angles of phases A and B. When the neutral point potential is OX, the input voltages of the energy meter voltage circuit are U uox 、U vox 、U wox , where U uox =U vox =190.53V, and Uwox =220V+110V=330V, the phase angle between phases A and B, the phase angle between phases B and C, and the phase angle between phases A and C are ∠UOXV, ∠VOXW, and ∠WOXU respectively. Obviously, the three angles at this time are no longer all equal to 120°.

[0129] Figure 7 The phenomenon of neutral point drift is explained using the example of N drifting in the W→N direction. It is understandable that point N can also drift in the V→N or U→N directions. Furthermore, once neutral point drift occurs, the three phase angles between the A, B, and C voltages will not all be 120°, and the voltage of one of the three phases may exceed 220V, indicating that the voltage exceeds the upper limit. In some embodiments, if at least one of the three phase angles between the three-phase voltages of the monitored energy meter is not equal to 120°, the three-phase voltage phase angle is considered to have drifted.

[0130] continue Figure 7 The neutral point drift example described, Figure 8 Figure 2 shows a schematic diagram of the phase sequence voltage and current angle drift caused by the neutral point drift in this example. Figure 8 As shown, taking phase A as an example, when there is no neutral point drift, the voltage of phase A is vector The current is represented by the vector Indicates that the phase sequence voltage and current angle of phase A at this time After the neutral point drifts, the A phase voltage is vector Indicates that, and relatively Drift angle Indicates 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 angle between the phase sequence voltage and current of phase B is given by ∠U VN OI VN becomes ∠U VO OI V That is, after neutral point drift occurs, the voltage and current angles of a single phase sequence will drift. Therefore, in some embodiments, when the voltage and current angles of any one of the three phase sequences drift, neutral point drift is determined to have occurred, thereby posing a risk of burnout to the zero line of the monitored electricity 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] Table 5

[0133] In some embodiments, in response to the first average voltage being greater than the first voltage setting value, the resistance value of the voltage divider resistor of the voltage sampling circuit of the monitored electric energy meter is obtained; in response to the resistance value of the voltage divider resistor being less than the resistance setting value, it is determined that there is a risk of burning of the terminal block of the monitored electric energy meter.

[0134] Figure 9 The following diagram illustrates the voltage sampling circuit for an energy meter chip. A fault in the meter's voltage sampling circuit causes the voltage to exceed the upper limit. Burned terminal blocks create carbonized impurities that adhere to resistors R201-R206 in the voltage sampling circuit, reducing the resistance of the voltage divider resistors. Consequently, the divided voltage V1+ input to the meter chip increases, resulting in a higher voltage value.

[0135] Because poor contact of the live or neutral wires can increase contact resistance, causing the voltage to exceed the lower limit, in some embodiments, in response to the first average voltage being less than the second voltage setting value, it is determined that the neutral wire and / or the live wire of the target phase sequence of the monitored electric energy meter have a burning risk.

[0136] In order to ensure that the acquired electricity meter data corresponds to the electricity meter of the target low-voltage user, in some embodiments, the acquired original electricity meter data is further filtered according to the wiring method of the electricity meter. Specifically, it can be: filtering electricity meters with wiring methods of three-phase four-wire and single-phase as the electricity meters of the target low-voltage user. In other embodiments, the user's electricity meter with error in the verification file is checked based on the acquired electricity meter data. For example, if the data acquired for an electricity meter shows that the wiring method of the electricity meter is three-phase four-wire, but the verification file of the electricity meter records single-phase wiring, the erroneous verification file needs to be corrected.

[0137] Figure 10 FIG. 1 is a block diagram showing the hardware configuration of the apparatus 100 for implementing the method for monitoring the hidden dangers of burning of metering equipment according to the embodiment of the present disclosure. Figure 10 As shown, the 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 executes the method for monitoring the hidden dangers of burning of metering equipment according to any of the above embodiments. Figure 10 In the device 100, only the components related to this embodiment are shown. Therefore, it is obvious to those skilled in the art that the device 100 may also include Figure 10The 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 comparison with the aforementioned embodiments in this specification, and can achieve the technical effects of the aforementioned embodiments, so they will not be repeated here.

[0138] The apparatus 100 may correspond to a computing device having various processing functions. For example, the apparatus 100 may be implemented as various types of devices, such as a personal computer (PC), a server device, a mobile device, and the like.

[0139] The processor 101 can control the operation of the device 100. For example, the processor 101 controls the operation of the device 100 by executing the program stored in the memory 102 on the device 100. The processor 101 can 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, the present disclosure is not limited to this. In the present embodiment, the processor 101 can be implemented in any appropriate manner. For example, the processor 101 can take the form of a computer-readable medium, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc., such as a microprocessor or a processor and a computer-readable program code (such as software or firmware) that can be executed by the (micro) processor.

[0140] Memory 102 can be hardware used to store various data and instructions processed by device 100. For example, memory 102 can store processed data and data to be processed by device 100. Memory 102 can store data sets that have been processed or are to be processed by processor 101, such as pending electricity meter data. Furthermore, memory 102 can store applications, drivers, and the like to be driven by device 100. For example, memory 102 can store various programs related to methods for monitoring metering equipment burnout hazards, which will be executed by processor 101. Memory 102 can be DRAM, but the present disclosure is not limited thereto. Memory 102 can include at least one of volatile memory and non-volatile memory. 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), and the like. The volatile memory may include dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), PRAM, MRAM, RRAM, ferroelectric RAM (FeRAM), etc. In an embodiment, the memory 102 may include at least one of a hard disk drive (HDD), a solid-state drive (SSD), a high-density flash memory (CF), 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.

[0141] The specific functions implemented by the memory 102 and processor 101 of the device 100 provided in the implementation manner of this specification can be explained in comparison with the aforementioned implementation manner in this specification, and can achieve the technical effects of the aforementioned implementation manner, so they will not be repeated here.

[0142] In summary, this disclosure is driven by business analysis algorithms as the core, based on the core theory of electrical basic principles, Ohm's law, and integrates big data correlation analysis, data distribution analysis, cluster analysis and other algorithms, and proposes a high-speed power line carrier (HPLC) phase recognition function that does not rely on the electric energy meter itself. By accurately capturing the changes in the voltage data of the substation and the user, and using the Pearson correlation coefficient algorithm, the phase of the substation is used as a solid benchmark to achieve accurate identification of the user's true phase. On this basis, on the one hand, the collected data of an electric energy meter is used to extract the time points when the load is close, and relying on Ohm's law, the cornerstone of physics, the loop resistance of the selected time points is accurately calculated, and the discreteness of the loop resistance is used to determine whether there are safety hazards when the user operates the metering equipment. On the other hand, this disclosure further accurately locks the adjacent electricity users under the same phase based on the latitude information and the box-meter relationship in the GIS geographic information system, and extracts the time points with similar loads for adjacent electric energy meters. Subsequently, relying on Ohm's law, a cornerstone of physics, the loop resistance and contact resistance differences of adjacent user electricity meters are accurately calculated. By comparing and analyzing the contact resistance differences, it is determined whether there are safety hazards in the user's metering equipment. In addition, this disclosure combines the single-variable anomaly detection method based on the Grubbs hypothesis with the LOF multivariate anomaly detection method to achieve comprehensive anomaly detection for voltage, current, and power data, eliminating data interference, improving data quality, and providing data support for effective model identification.

[0143] The method proposed in this disclosure subdivides the operating status of low-voltage user metering equipment into three diagnostic results: poor neutral wire contact, poor live wire contact, and suspected equipment burnout failure. It opens up a new innovative method for monitoring and analyzing safety hazards in the operation of low-voltage user metering equipment; thereby promoting the transformation from the traditional "post-processing" model to the advanced management model of "in-process early warning and monitoring", realizing early detection, early prevention, and early processing of metering equipment failures, thereby greatly reducing user power outage time caused by equipment failures, significantly improving users' electricity satisfaction and sense of security, and laying a solid foundation for building a safer, more stable, and efficient power supply environment.

[0144] It should be noted that, for the purpose of simplicity, the present disclosure describes some methods and embodiments thereof as a series of actions and combinations thereof, but those skilled in the art will understand that the scheme of the present disclosure is not limited by the order of the actions described. Therefore, based on the disclosure or teachings of the present disclosure, those skilled in the art will understand that some of the steps therein can be performed in other orders or simultaneously. Further, those skilled in the art will understand that the embodiments described in the present disclosure can be regarded as optional embodiments, that is, the actions or modules involved therein are not necessarily necessary for the implementation of one or more schemes of the present disclosure. In addition, depending on the different schemes, the description of some embodiments of the present disclosure also has different emphases. In view of this, those skilled in the art will understand that the parts that are not described in detail in a certain embodiment of the present disclosure may also refer to the relevant descriptions of other embodiments.

Claims

1. A method for monitoring hidden dangers of burning of metering equipment, comprising: Confirm the monitored electric energy meter corresponding to the monitored metering equipment; Acquiring data of the monitored electric energy meter to obtain monitored meter data, wherein the monitored meter data includes voltage data and current data of the monitored meter; Identifying the phase sequence of the voltage data and / or the current data of the monitored meter to obtain a phase sequence identification result; Extracting data corresponding to a target phase sequence from the monitored meter data according to the phase sequence identification result to obtain first phase sequence data; Determining whether the metering device has a potential burning risk based on the first phase sequence data; The monitored meter data also includes monitored meter location data; The step of determining whether the metering device has a potential burnout risk based on the first phase sequence data includes: determining at least two reference electric energy meters according to the location data of the monitored meter and the load of the monitored meter; Acquiring data of the reference electric energy meter at the target phase sequence to obtain reference meter phase sequence data; According to the first phase sequence data and the phase sequence data in the reference table, it is determined whether the metering equipment has a burning risk.

2. The method according to claim 1, wherein judging whether the metering device has a burning risk based on the first phase sequence data and the phase sequence data in the reference table comprises: Calculating a contact resistance difference between the monitored electric energy meter and the reference electric energy meter at a set time according to the first phase sequence data and the reference meter phase sequence data to obtain a first contact resistance difference; Calculating the contact resistance difference between the reference electric energy meters at a set time according to the phase sequence data of the reference meter to obtain a second contact resistance difference; It is determined whether the metering device has a potential burning risk according to the first contact resistance difference and the second contact resistance difference.

3. The method according to claim 1, wherein the monitored meter data further includes monitored meter location data; Determining at least one test electric energy meter for the monitored electric energy meter according to the location data of the monitored electric energy meter and the load of the electric energy meter; Calculating the average voltage of the target phase sequence within a set time range for the monitored electric energy meter to obtain a first average voltage; Calculating the average voltage of the target phase sequence within a set time range for the inspection electric energy meter to obtain a second average voltage; wherein, after determining whether the metering device has a potential burnout risk based on the first phase sequence data, the method further includes: A determination result is checked based on the first average voltage and the second average voltage.

4. The method of claim 3, further comprising: In response to the first average voltage being greater than a first voltage setting value, Calculate the phase angle of the three-phase voltage of the monitored electric energy meter; In response to a phase angle drift of the three-phase voltage, It is confirmed that there is a hidden danger of burning of the neutral wire of the monitored electric energy meter.

5. The method of claim 3, further comprising: In response to the first average voltage being greater than a first voltage setting value, Obtaining the resistance value of the voltage divider resistor of the voltage sampling circuit of the monitored electric energy meter; In response to the voltage dividing resistor having a resistance value less than a resistance setting value, It is determined that the terminal block of the monitored electric energy meter has a burning risk.

6. The method of claim 3, further comprising: In response to the first average voltage being less than a second voltage setting value, It is determined that the zero line of the monitored electric energy meter and / or the live line of the target phase sequence have a burning risk.

7. The method of claim 1, wherein: Acquiring data of the monitored electric energy meter to obtain the monitored meter data includes: Obtaining raw data of the monitored electric energy meter; Outlier detection and / or missing value interpolation are performed on the original data to obtain the monitored table data.

8. A device for monitoring the hidden dangers of burning of metering equipment, comprising: a processor configured to execute program instructions; as well as A memory configured to store the program instructions, which, when loaded and executed by the processor, causes the apparatus to perform the method according to any one of claims 1 to 7.

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