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

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 promotion and application of IoT meters and 2.0 version smart meters, as well as the continuous improvement of the informationization and automation level of the new generation marketing 2.0 system and the electricity information collection 2.0 system, it provides a strong data support and technical foundation for the intelligent monitoring and analysis of safety hazards in the operation of metering equipment. In particular, with the comprehensive upgrade of the electricity information collection 2.0 system, the collection capacity of load curve data of low-voltage user electric energy meters has achieved a qualitative leap in density, frequency, integrity and timeliness, laying a solid foundation for big data application analysis. The system has demonstrated strong monitoring and early warning efficiency in the fields of power outage management, power theft detection and intelligent operation and maintenance. By collecting the operation data of the electric energy meter, such as voltage, current, power and other data, combined with business experience, it can realize reliable analysis of overload, voltage abnormality and other situations, and warn of possible overheating or burning risks. However, in terms of using multi-dimensional data features and deep correlation analysis to identify the hidden dangers of burning of metering equipment, the domestic industry still lacks efficient and accurate analysis methods and technical means, and the potential in this field has not yet been fully released and tapped.

[0005] 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 and accurately identify the hidden dangers of burning of the measuring equipment, thereby preventing the occurrence of burning accidents of the measuring equipment. Summary of the invention

[0006] 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.

[0007] In the first aspect, the present disclosure proposes a method for monitoring the hidden danger of burning of metering equipment, including: confirming the monitored electric energy meter corresponding to the monitored metering equipment; acquiring the data of the monitored electric energy meter to obtain the monitored meter data, the monitored meter data including the monitored meter voltage data and the 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 the data corresponding to the target phase sequence from the monitored meter data to obtain the first phase sequence data; based on the first phase sequence data, judging whether the metering equipment has the hidden danger of burning.

[0008] In a second aspect, the present disclosure proposes a device for monitoring the potential burnout of a metering device, 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

[0009] By reading the detailed description below 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 understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting 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 metering devices in some embodiments of the present disclosure in a power supply area.

[0011] Figure 2 An exemplary flow chart of a method for monitoring the burning risk of a metering device in some embodiments of the present disclosure is shown.

[0012] Figure 3 This is a conceptual diagram explaining the reachable distance.

[0013] Figure 4 An exemplary flow chart of a method for determining whether a metering device has a potential burning risk according to first phase sequence data in some embodiments of the present disclosure is shown.

[0014] 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.

[0015] 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 reference table phase sequence data in some embodiments of the present disclosure is shown.

[0016] Figure 7 A neutral point drift interpretation diagram is shown by way of example.

[0017] Figure 8 The schematic diagram exemplarily shows that the neutral point drift causes the phase sequence voltage and current angle drift.

[0018] Fig. 9 The voltage sampling circuit diagram of the electric energy meter measurement chip is shown as an example.

[0019] Fig.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

[0020] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0021] 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 exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

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

[0023] As used in this specification and claims, the term "if" may 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" may 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.

[0024] The specific implementation of the present disclosure is described in detail below with reference to the accompanying drawings.

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

[0026] Figure 1 FIG. 1 is an exemplary schematic diagram showing the connection relationship of metering equipment in a 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; the three-phase electricity is composed of three alternating currents with the same frequency, equal amplitude, and phase difference of 120°, 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.

[0027] like Figure 1 The power supply system shown in FIG. 1 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, three-phase energy meter B, C) in the power supply system are used to measure the amount of energy consumed by each or part of the users. In this power supply system, a single-phase power supply can be drawn from any phase line and neutral line to supply power to the single-phase load; and for three-phase loads, a three-phase power supply can be drawn from three phase lines and neutral line to supply power to the three-phase load. Corresponding to single-phase loads and three-phase loads, user energy meters can be divided into single-phase energy meters and three-phase energy meters.

[0028] 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 composed of any one 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 composed of three phase lines and a neutral line. The knife switch is used to control the on and off of the circuit. In 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.

[0029] In a power supply area, power can be supplied to a large number of low-voltage users. For example, all residents in a community 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.

[0030] As the usage time increases, various components of the metering equipment gradually age and the insulation performance decreases, which are prone to leakage, short circuit and other problems, thus causing 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 have 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 substation total meter; in the data collected by the three-phase electric energy meter, the A, B, and C phases default to the A, B, and C phases of the substation total meter respectively; however, in practice, the phase sequence of the collected data in the user's electric energy meter and the phase sequence of the substation total meter do not necessarily conform to the default value. The 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, when 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 master 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 it is even more impossible to perform in-depth data analysis.

[0031] 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 the smart electric energy meter during operation and accurately identify the hidden dangers of burning of the metering equipment.

[0032] Figure 2 An exemplary flow chart of a method for monitoring the burning risk 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, and 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 risk of burning out based on the first phase sequence data.

[0033] For 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 electric energy meter. The monitored metering device refers to the metering device that needs to be judged whether it has a hidden danger of burning. Accordingly, the electric energy meter corresponding to the monitored metering device is the monitored electric energy meter.

[0034] For step 202, it can be understood that the monitored meter data can include operation data. Operation data is the time series data generated by the electric energy meter during the actual operation process, including active power data, voltage data, current data, electricity data, substation line loss rate data, etc. The method of obtaining the operation data can be to obtain various operation data once at a fixed time interval. For example, real-time data such as active power, current, and voltage can be obtained at the same time at every hour of the 24 hours of each day, so as to obtain various operation 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 operation data includes the historical operation data of the monitored electric energy meter in the past week, the past three months, or the past year.

[0035] Continuing with step 202, in some embodiments, the data of the monitored electric energy meter may also include: archival data in the new generation of electricity consumption information collection 2.0 system, marketing 2.0 system, user GIS coordinate data, and user electric energy meter box-meter relationship data; wherein, the archival data is mainly about static information such as the properties and installation and use of the electric energy meter itself, such as station capacity, comprehensive ratio, station type, user information, metering point information, and electric energy meter information; the electric energy meter information may include the electric energy meter model, manufacturer, installation time, and asset number.

[0036] For 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, and the phase sequence of the single-phase electric energy meter is identified to obtain the phase sequence identification result, that is, to determine whether the data collected by the electric energy meter corresponds to the 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, and the phase sequence of the three-phase electric energy meter is identified to obtain the phase sequence identification result, that is, to determine the correspondence between the three-phase data collected by the electric energy meter and the 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 carry phase sequence information, so the phase sequence identification result can be obtained based on the voltage data or the current data, or in combination with the voltage data and the current data.

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

[0038] Therefore, the method for monitoring the burning risk of metering equipment provided by the present disclosure can extract the phase sequence data of the monitored meter corresponding to the phase sequence of the total meter of the area by identifying the phase sequence of the electric energy meter of the monitored metering equipment. Since the identification of the burning risk is performed on the phase sequence data of the monitored meter, it can be determined whether there is a burning risk in the power supply circuit corresponding to the target phase sequence, thereby not only accurately identifying the burning risk of the metering equipment, but also facilitating the location of the burning risk.

[0039] In some embodiments, collecting data for a certain phase of a single-phase electric energy meter or a three-phase electric energy meter and identifying the phase sequence 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 total meter.

[0040]

[0041] In formula (1), z i It represents the voltage or current data of a phase in the total table of the substation area. 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 calculates the variance. Then It represents the correlation coefficient between the voltage or current data of a certain phase of the substation main 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 main meter 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 user's electric energy meter collected data and the phase voltage data of the substation total meter can be:

[0043]

[0044] 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; 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' hypothesis test is used to detect and remove outliers from the univariate time series in the original data. It is understandable 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.

[0051] Grubbs'Test is a hypothesis testing method, which is often used to test 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 are no outliers in the data set; H1: There is an outlier in the data set. The algorithm flow of using Grubbs'Test to detect anomalies in a univariate time series x is:

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

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

[0054] 3. Find the standard score of suspicious values x_s is a suspicious value. If z_score is greater than the Grubbs critical value, then the suspicious value is an outlier. The Grubbs critical value is obtained by looking up the table.

[0055] In some embodiments, a method based on a local outlier factor (LOF) is used to detect and remove outliers from a multivariate time series in the original data. It is understandable that for a single-phase or three-phase electric energy 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 the points. The farther the distance between the 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 the points globally, it is a "local outlier factor".

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

[0057] ① The 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.

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

[0059] ③ The reachable distance of point p relative to point o: the kth reachable distance from point p to point o = max(the k nearest neighbor distance of point o k_distance(o), the distance from point p to point o ‖po‖).

[0060] Figure 3 The following is an exemplary concept diagram of reachable distance, such as Figure 3 As shown, 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 circle 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, 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 k-th 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‖.

[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 reachable distance of the k nearest neighbors of point p (i.e., the reciprocal of the average of all reachable distances in the kth distance neighborhood of point p). The larger the value, the more compact the data is.

[0062]

[0063] 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).

[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 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.

[0065]

[0066] 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 reachable density of each point o in (p).

[0067] In the data collection of the power consumption information collection system, data missing is an inevitable and must-face problem. In particular, the missing of voltage, current, power and other data will directly affect the accuracy of mining and prediction of abnormal clues of operation safety hazards. Therefore, the processing of missing data becomes very important.

[0068] In some embodiments, the missing attribute is used as the dependent variable, and other attributes are used as independent variables. The missing value interpolation is performed using the n-order polynomial approximation method. The interpolation process is as follows:

[0069] The first step is to select the third-order (or fifth-order) fitting equation. Assume that the function fitting equation between the time series data (such as voltage and current, etc.) x and the attribute time t is:

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

[0071] The second step is to select the time series data [x n , t n ] before and after the 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.

[0072] The third step is to use the least squares principle to make the interpolation [x n , t n ] value reaches the minimum, and the values ​​of coefficients a0, a1, a2, and a3 are solved, that is, the mean square error equation shown in formula (5) is solved:

[0073]

[0074] For the coefficients a0, a1, a2, and a3, a matrix calculation is established as shown in formula (6):

[0075]

[0076] 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):

[0077]

[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 burning risk according to 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, judging whether the metering device has a burning risk according to 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, judging whether the metering device has a burning risk according to the discreteness of the loop resistance.

[0082] The load of the electric energy meter can be measured by the current and power data of the electric energy meter. As mentioned above, the monitored meter data can be multivariable time series data. Then, for the data at each moment, the load of the electric energy meter at that moment can be measured according to its current or power value, so that according to the load of the electric 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, effective data with power load is filtered out 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 It represents the absolute value of the current at time t in D1, and its unit is ampere (A). K is the comprehensive multiplier of the electric energy meter. The comprehensive multiplier is the multiple multiplied when the electric energy meter measures electric energy, and is determined by the product of the current transformer ratio and the voltage transformer ratio. According to formula (8), the data at the time point with a small load can be filtered out from the first phase sequence data, thereby retaining the data at the time point with an 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 higher 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 value and minimum value of x, respectively. According to formula (9), the second phase sequence data with higher 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 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 the present disclosure have found that: for metering equipment without a risk of burning, under conditions where the load is close, the loop resistance value is basically stable and unchanged; while for metering equipment with a risk of burning, even under conditions where the load is close, there will be a sudden inflection point in the loop resistance value before and after the risk of burning occurs, that is, the loop resistance value fluctuates. Therefore, it is possible to determine whether the metering equipment has a risk of burning 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 may be set, and by comparing the calculated discreteness value of Rs with the discreteness threshold, it is determined whether the metering device has a burning risk. In some embodiments, the discreteness threshold may be set to 1, and when S>1, it is determined that the loop resistance value has a certain volatility, thereby indicating that there is a burning risk in the metering device loop.

[0095] The inventors of the present disclosure have discovered that: in a power supply substation, each phase of the substation master meter needs to supply power to multiple electric energy meters. For the data of a certain phase sequence of the monitored electric energy meter, the data of other electric energy 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 electric energy meter, so as to determine whether there is a hidden danger of burning of the monitored metering equipment. Exemplarily: when the data of other electric energy meters on the target phase sequence are relatively close, and only the data of the monitored electric energy meter on the target phase sequence appears as an outlier, then there is a greater degree of confidence that the monitored electric energy meter has a hidden danger of burning.

[0096] Figure 5 An exemplary flow chart of a method for determining whether a metering device has a burning risk according to the first phase sequence data in other embodiments of the present disclosure is shown. It can be understood that Figure 5 This is another specific implementation of the above step 205. Figure 5 As shown, in some embodiments, judging whether the metering device has a burning risk according to the first phase sequence data includes: step 501, the monitored meter data also includes the monitored meter position data, and at least two reference electric energy meters are determined according to the monitored meter position data and the load of the monitored meter. Step 502, obtaining the data of the reference electric energy meter at the target phase sequence to obtain the reference meter phase sequence data. Step 503, judging whether the metering device has a burning 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 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 and has a similar or identical load to the monitored electric energy meter can be determined for the monitored electric energy meter. The close loads 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 that are adjacent in location 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 longitude and latitude 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 the fields of 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 average radius of the earth, which is 6371 kilometers. In some embodiments, a distance threshold can be set, and other electric energy meters whose distance to the monitored electric energy meter is less than the set threshold are regarded as neighboring electric energy meters. As for the distance threshold value, it can be 10m.

[0101] The above-mentioned embodiments determine the neighboring electric energy meters for the monitored electric energy meter based on the meter box GIS longitude and latitude coordinate data. In other embodiments, the neighboring electric energy meters for the monitored electric energy meter are determined based on the box-meter relationship in the marketing field operation system. In a power supply area, a distribution box is usually connected to multiple electric energy meters, so the electric energy meters connected to the same distribution box can be considered as neighboring electric energy meters. In other embodiments, the neighboring electric energy meters for the monitored electric energy meter are determined based on the meter box GIS longitude and latitude coordinate data 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 electric energy 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; according to the reference table phase sequence identification result, extracting the data corresponding to the target phase sequence from the reference table data to obtain the reference table phase sequence data. This part of the content has been introduced in sufficient detail in the previous text and will not be repeated here.

[0103] As for step 503, it can be understood that the monitored electric energy meter and the reference electric energy meter are similar in position and load. In the case where there is no risk of burning of the metering equipment, the data of each electric energy meter at the same phase sequence should also be relatively close, while for the metering equipment with the risk of burning, its phase sequence data will show outlier characteristics. Therefore, it can be judged whether the metering equipment has the risk of burning according to the first phase sequence data and the second phase sequence data.

[0104] Figure 6 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 is shown. It can be understood that Figure 6 This is a specific implementation of the above step 503. Figure 6 As shown, in some embodiments, judging whether the metering device has a burning risk according to 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 according to 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 the reference electric energy meters at a set time according to the reference table phase sequence data to obtain a second contact resistance difference. Step 603, judging whether the metering device has a burning risk according to 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, contact resistance and the wire resistance between the substation total meter and the user's electric energy meter. The contact resistance reflects the contact condition of the wire in the metering equipment. High contact resistance means poor contact of the wire, which poses a risk of burning. For two adjacent electric energy meters, the wire resistance from the substation total 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 according to the first phase sequence data and the reference table phase sequence data, respectively, and 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 resistances of different reference electric energy 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 electric energy meter and the reference electric energy meter, and 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, and 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 risk of burning.

[0107] For example, taking low-voltage user A as an example, its electric energy meter number is XX3368, and the collected data of phase A of user A's electric energy meter on a certain day are selected for analysis, and the data described in Table 2 are obtained. It can be seen from Table 2 that 21:00 is the minimum load time of user A's electric energy meter, and 20:00 is the maximum load time of user A's electric energy meter. However, at 20:00, there is only one user adjacent to user A and with a similar load, which cannot provide a reference for judging whether the contact resistance of user A's electric energy meter is abnormal. At 21:00, there are 3 users adjacent to user A and with similar loads, corresponding to electric energy meters XX6288, XX2801, and XX5428, respectively. The three electric energy meters constitute the reference electric energy meters for user A's electric energy meter. Extract the phase A data of the three reference electric energy meters at 21:00, as shown in formulas (13) and (14), and calculate the loop resistance of user A's electric energy meter and reference electric energy 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 value is 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 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; It 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; They 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. Among them, when the set threshold of the contact resistance difference is 2 ohms, since the maximum value of the contact resistance difference between the three reference electric energy meters is 1.82 ohms and the minimum value is 0.5 ohms, both of which do not exceed the set threshold, it can be considered that the three reference electric energy meters are operating safely and there is no risk of burning; and the difference in contact resistance between the electric energy meter of user A and the three reference electric energy meters is 6.14 ohms, 7.96 ohms, and 7.64 ohms, respectively, all exceeding the set threshold, it can be considered that the monitored metering equipment has a risk of burning.

[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 can 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 line, 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 line or the live line. 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 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 a 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 a 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 risk of burning out 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 understandable that, according to the location data of the monitored meter and the load of the monitored meter, a test meter with a location close to the monitored meter and a load close to the monitored meter can be determined. The method for determining the test meter is the same as the method for determining the reference meter, which will not be repeated here.

[0121] Exemplarily, for the aforementioned user A, after determining that its metering equipment has a risk of burning, as shown in Table 4, three inspection energy meters are found for it (corresponding to adjacent users 5, 6, and 7, respectively). Using the voltage data of the energy meter of user A and the inspection energy meter for 7 days, the average voltage of the energy meter of user A within 7 days is calculated to obtain the first average voltage, and the average voltage of the inspection energy 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 energy meter at the target phase sequence is abnormal is correct, and then the result of judging that the monitored energy meter has a risk of burning based on the abnormal resistance 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 low measured access voltage value due to the high contact resistance value. Based on this, for the monitored electric energy meter judged to be abnormal in resistance, the electric energy meter adjacent to the position under 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 according to the monitored meter position data; the average voltage of the target phase sequence in the no-load period of the set length is calculated for the verification electric energy meter to obtain the third average voltage; the average voltage of the target phase sequence in the no-load period of the set length is calculated for the monitored electric energy meter to obtain the fourth average voltage; wherein, after judging whether the metering equipment has a hidden danger of burning according to the first phase sequence data, it also 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 a 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 a neutral point 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 respectively) 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 ∠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 O1 and finally to point OX. They represent the voltage drift angles of phases A and B respectively. When the neutral point potential is OX, the input voltages of the voltage circuit of the energy meter 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 Taking the drift of N in the direction of W→N as an example to explain the phenomenon of neutral point drift, it can be understood that point N can also drift in the directions of V→N and U→N, and: once the 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 one phase among the three phases will exceed 220V, that is, the voltage exceeds the upper limit. In some embodiments, for the three phase angles between the three-phase voltages of the monitored electric energy meter, when at least one of the phase angles is not equal to 120°, it is considered that the phase angle of the three-phase voltage has drifted.

[0130] continue Figure 7 The neutral point drift example described is, Figure 8 FIG. 1 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 expressed as a 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 phase sequence voltage and current angle of phase B is given by ∠U VN OI VN becomes ∠U VO OI V That is to say, after the 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 drift, it is determined that the neutral point drift occurs, so that the zero line of the monitored electric energy meter has a risk of burning. Table 5 shows the relationship between the voltage drift angle of phases A and B and the three-phase voltage value after the 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 a first voltage setting value, the resistance value of a voltage divider resistor in a 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 a resistance setting value, it is determined that there is a risk of burning of the terminal block of the monitored electric energy meter.

[0135] Fig. 9 The voltage sampling circuit diagram of the energy meter chip is shown as an example. The voltage exceeds the upper limit due to the fault of the energy meter voltage sampling circuit. Due to the burning of the terminal block, carbonized impurities are attached to the voltage sampling circuit resistors R201 to R206, resulting in a decrease in the resistance of the voltage divider resistor. Therefore, the voltage divider V1+ input to the metering chip becomes higher, so the voltage value obtained by the metering chip is too high.

[0136] Poor contact of the live wire or the neutral wire will increase the contact resistance, thereby 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 of the monitored electric energy meter and / or the live wire of the target phase sequence have a burning risk.

[0137] In order to ensure that the acquired electric energy meter data is the data corresponding to the electric energy meter of the target low-voltage user, in some embodiments, the acquired original electric energy meter data is further screened according to the wiring method of the electric energy meter, specifically, it can be: screening electric energy meters with wiring methods of three-phase four-wire and single-phase as the electric energy meters of the target low-voltage user. In other embodiments, the electric energy meter of the user with an error in the verification file is checked based on the acquired electric energy meter data. For example, the data acquired for an electric energy meter shows that the wiring method of the electric energy meter is three-phase four-wire, while the verification file of the electric energy meter records single-phase wiring. At this time, the wrong verification file needs to be corrected.

[0138] Fig.10 The block diagram shows the hardware configuration of the device 100 that can implement the method for monitoring the hidden danger of burning of metering equipment according to the embodiment of the disclosure. Fig.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 burning hazard of metering equipment according to any one of the above embodiments. Fig.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 Fig.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.

[0139] 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, etc.

[0140] 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 a 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, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, 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.

[0141] The memory 102 can be used to store hardware of various data and instructions processed in the device 100. For example, the memory 102 can store processed data and data to be processed in the device 100. The memory 102 can store data sets that have been processed or to be processed by the processor 101, such as electric energy meter data to be processed, etc. 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 the method for monitoring the hidden dangers of burning of metering equipment to be executed by the processor 101. The memory 102 can be a DRAM, but the present disclosure is not limited thereto. The memory 102 can include at least one of a volatile memory or a non-volatile memory. The non-volatile memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a phase change RAM (PRAM), a magnetic RAM (MRAM), a resistive RAM (RRAM), a ferroelectric RAM (FRAM), etc. 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.

[0142] The specific functions implemented by the memory 102 and the processor 101 of the device 100 provided in the implementation mode of this specification can be explained in comparison with the aforementioned implementation modes in this specification, and can achieve the technical effects of the aforementioned implementation modes, and will not be repeated here.

[0143] In summary, this disclosure is 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. It 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 total meter of the substation and the user, using the Pearson correlation coefficient algorithm, and taking the phase of the total meter of the substation as a solid benchmark, it realizes the accurate identification of the user's real phase. On this basis, on the one hand, the collected data of an electric energy meter is used to extract the time point when the load is close, relying on Ohm's law, the cornerstone of physics, to accurately calculate the loop resistance at the selected time point, and judge whether there is a safety hazard in the user's operation of the 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 based on the latitude information and the box-meter relationship in the GIS geographic information system, and extracts the time point with similar load for the adjacent electric energy meters. Subsequently, relying on Ohm's law, the cornerstone of physics, the loop resistance and contact resistance difference of the adjacent user's electric energy meter are accurately calculated. By comparing and analyzing the contact resistance difference, it is determined whether there are safety hazards in the user's metering equipment. In addition, this disclosure comprehensively applies the single variable anomaly detection method based on the Grubbs hypothesis and the LOF multivariate anomaly detection method to achieve comprehensive anomaly detection of voltage, current, and power data, eliminate data interference, improve data quality, and provide data guarantee for the effective identification of the model.

[0144] The method proposed in this disclosure subdivides the operating status of low-voltage user metering equipment into three diagnostic results: poor neutral line contact, poor live line 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 "after-the-fact processing" mode to the advanced management mode of "in-process early warning and monitoring", achieving 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.

[0145] 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 appreciate that the scheme of the present disclosure is not limited by the order of the actions described. Therefore, based on the disclosure or teaching of the present disclosure, those skilled in the art will appreciate that some of the steps therein may be performed in other orders or simultaneously. Further, those skilled in the art will appreciate that the embodiments described in the present disclosure may be regarded as optional embodiments, i.e., the actions or modules involved therein may not necessarily be necessary for the implementation of one or some of the schemes of the present disclosure. In addition, depending on the different schemes, the present disclosure also has different focuses on the description of some embodiments. In view of this, those skilled in the art may appreciate the parts that are not described in detail in a certain embodiment of the present disclosure, and may also refer to the relevant descriptions of other embodiments.

Claims

1. A method for monitoring the hidden danger of burning of metering equipment, comprising: Confirm the monitored electric energy meter corresponding to the monitored metering equipment; Acquire data of the monitored electric energy meter to obtain monitored meter data, wherein the monitored meter data includes monitored meter voltage data and monitored meter current data; Identify 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 the target phase sequence from the monitored meter data to obtain first phase sequence data; It is determined whether the metering device has a potential burning risk according to the first phase sequence data.

2. The method according to claim 1, wherein judging whether the metering device has a burning risk according to the first phase sequence data comprises: 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 at the target phase sequence according to the second phase sequence data; According to the discreteness of the loop resistance, it is judged whether the metering device has a hidden danger of burning.

3. The method according to claim 1, wherein the monitored meter data further comprises monitored meter location data; The step of judging whether the metering device has a burning risk according to 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; Acquire the data of the reference electric energy meter at the target phase sequence to obtain the reference table 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 device has a burning risk.

4. The method according to claim 3, wherein judging whether the metering device has a burning risk according to the first phase sequence data and the reference table phase sequence data comprises: Calculating the 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; Whether the metering device has a potential burning risk is determined according to the first contact resistance difference and the second contact resistance difference.

5. The method of claim 1, wherein the monitored meter data further comprises monitored meter location data; Determine at least one inspection 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; The average voltage of the target phase sequence within a set time range is calculated for the inspection electric energy meter to obtain a second average voltage; wherein, after judging whether the metering device has a burning risk according to 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.

6. The method of claim 5, 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 the neutral line of the monitored electric energy meter has a potential burnout risk.

7. The method of claim 5, 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.

8. The method of claim 5, further comprising: In response to the first average voltage being less than a second voltage setting value, It is determined that there is a burning risk of the neutral line of the monitored electric energy meter and / or the live line of the target phase sequence.

9. The method of claim 1, wherein: Acquiring the data of the monitored electric energy meter to obtain the monitored meter data includes: Acquiring original data of the monitored electric energy meter; The original data is subjected to outlier detection and / or missing value interpolation to obtain the monitored table data.

10. A device for monitoring the hidden danger 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 device to perform the method according to any one of claims 1 to 9.

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