A smart verification and traceability method and system applied to an electric energy meter

Abstract

A kind of intelligent verification traceability method and system applied to electric energy meter, comprising: obtaining the effective error of electric energy meter at each time in steady-state measurement working condition in set period, and calculating the excess error sequence of the excess error sequence;Modal energy features and power spectrum features are extracted from the effective error sequence, input fault recognition algorithm for fault judgment, and the electric energy meter preliminarily judged as fault is output;The mapping function between the operating parameters of different types of electric energy meters and the fixed drift is fitted;The current operating parameters of the electric energy meter preliminarily judged as fault are brought into the corresponding mapping function to obtain the theoretical inherent drift;The contact resistance deviation of the electric energy meter preliminarily judged as fault is calculated;The effective error of the electric energy meter preliminarily judged as fault is subtracted by the theoretical inherent drift and then subtracted by the contact resistance deviation to residual error, if the absolute value of residual error exceeds the set residual error threshold, it is finally determined as fault, the sensitivity and generalization ability of the present application are improved.

Description

Technical Field

[0001] This invention belongs to the field of electricity meter verification technology, and more specifically, relates to an intelligent verification and traceability method and system for electricity meters. Background Technology

[0002] With the development of smart grids, the number of in-service electricity meters has surged. The traditional management model that relies on manual on-site verification and replacement upon expiration is not only extremely labor-intensive and inefficient, but also makes it difficult to detect latent metering error drift caused by component aging or environmental stress in a timely manner. This fails to meet the stringent requirements of the power system for high reliability and real-time metering data.

[0003] Furthermore, due to environmental factors and aging lines, some electricity meters are showing varying degrees of degradation, making it difficult to accurately verify them from complex metering signals. Existing electricity meter error estimation algorithms suffer from poor real-time performance and low accuracy, failing to meet the requirements for real-time error verification of large-scale electricity meters. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a smart verification and traceability method and system for electricity meters.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of this invention proposes a smart verification and traceability method for electricity meters, comprising: The metering error of the energy meter at each moment under steady-state metering conditions within a set period is obtained. The metering error under non-steady-state metering conditions is regarded as a missing value. The missing value is filled in by interpolation. The metering error of the energy meter at each moment after filling in the missing value is a valid error. All valid errors form a valid error sequence. The excess of the valid error sequence is calculated to form an excess sequence. Modal energy features and power spectrum features are extracted from the effective error sequence; the out-of-error sequence, modal energy features and power spectrum features of each energy meter are input into the pre-trained fault identification algorithm for fault judgment, and the energy meters that are initially judged to be faulty are output. All electricity meters in the power grid are clustered according to their model and rated parameter characteristics to obtain different types of electricity meters. For each type of electricity meter, multiple standard electricity meters with the same model characteristics as the electricity meter corresponding to the cluster center are selected as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter is fitted. Substitute the current operating parameters of the energy meter initially identified as faulty into the corresponding mapping function to obtain the theoretical inherent drift; calculate the contact resistance and contact resistance deviation of the energy meter initially identified as faulty; if the contact resistance is greater than the set resistance threshold, it is determined that there is a loose connection between the energy meter terminals and the wires. The effective error of the energy meter initially judged to be faulty is subtracted from the theoretical inherent drift and then from the contact resistance deviation to obtain the residual. If the absolute value of the residual exceeds the set residual threshold, it is finally judged to be faulty.

[0007] Preferably, the metering error of the energy meter is obtained at each moment under steady-state metering conditions within a set period, specifically as follows: Obtain the actual voltage, actual current, and power factor of the power grid, and multiply the actual voltage, actual current, and power factor of the power grid to obtain the reference active power; Obtain the instantaneous measured power of the electricity meter, and divide the difference between the instantaneous measured power and the reference active power by the reference active power to obtain the measurement error; The metering error of the electricity meter at each moment under steady-state metering conditions is selected. The metering error under non-steady-state metering conditions is regarded as a missing value and the missing value is filled by interpolation.

[0008] Preferably, the steady-state metering condition specifically includes: The absolute value of the load change rate is lower than the set load change threshold; and the absolute value of the ambient temperature change rate is lower than the set environmental parameter change threshold; and the harmonic distortion rate of the actual voltage or current of the power grid is less than the corresponding set harmonic distortion rate threshold; and the actual current of the power grid is greater than the set proportion of the rated current. If the ratio of the total number of times in steady-state metering condition within a set period to the total number of times within a set period is less than the set ratio threshold, it indicates that the current period is an unstable window, and no fault judgment will be performed in the current period.

[0009] Preferably, the excess sequence is composed of the excesses of the effective error sequence, specifically as follows: Determine whether the effective error of the electricity meter at each moment is within the allowable error range corresponding to the accuracy class of the electricity meter. If it is, the error at the corresponding moment is 1; otherwise, it is 0.

[0010] Preferably, modal energy features and power spectrum features are extracted from the effective error sequence, specifically as follows: Variational Mode Decomposition (VMD) is performed on the effective error sequence to obtain the Individual Mode Format (IMF) of each mode sequence. The sum of the squared amplitudes of each IMF at each time step is calculated to obtain the energy of each IMF. The energy of each IMF is divided by the sum of the energies of all IMFs to obtain the energy ratio of each IMF as the mode energy feature. Each mode sequence IMF is converted into a power spectrum sequence using Fourier transform. For each mode sequence IMF, the center frequency of the corresponding mode sequence IMF is subtracted from the frequency corresponding to the m-th sampling point. The square of the difference is multiplied by the value corresponding to the m-th sampling point in the power spectrum sequence to obtain the frequency deviation weighted power of the m-th sampling point. The frequency deviation weighted power of all sampling points within the set period is superimposed and divided by the sum of the values ​​corresponding to all sampling points in the power spectrum sequence. The square root of the division result is used as the average spectral radius as the power spectrum feature.

[0011] Preferably, all electricity meters in the power grid are clustered according to their model and rated parameter characteristics to obtain different types of electricity meters, specifically: Model characteristics include: nameplate code and manufacturer; rated parameter characteristics include: accuracy class, basic current, median of rated operating temperature range, mean error under steady load, and standard deviation of steady load error; Clustering uses the K-medoids algorithm; during clustering, if two energy meters have the same nameplate code, the nameplate code matching cost is 0, otherwise it is a set value; if two energy meters have the same manufacturer, the manufacturer matching cost is 0, otherwise it is a set value; the set value is less than 1. After normalizing the rated parameter features, a rated parameter feature vector is formed. The square of the difference between the norms of the rated parameter feature vectors of two energy meters is calculated, plus the square of the nameplate code matching cost, plus the square of the manufacturer matching cost. The square root of the sum is used to obtain the cluster distance.

[0012] Preferably, a mapping function is fitted between the operating parameters and the fixed drift of the corresponding type of electricity meter, specifically as follows: Operating parameters include load, ambient temperature, and usage time; these parameters are combined into an operating parameter vector. A set number of kernel basis functions are weighted and superimposed, and the superposition result is added with a global bias value to obtain a fixed drift; the bandwidth parameter, center coordinates and weights of each kernel basis function are all fitted parameters; The center coordinates are a vector composed of the reference values ​​of the load, the ambient temperature, and the usage time. The kernel basis function is calculated by multiplying the square of the norm of the running parameter vector and its corresponding center coordinates, multiplying it by the corresponding bandwidth parameter, and then multiplying it by -1 as the exponent. The natural base e is used as the base for the exponentiation operation, and the result is the kernel basis function.

[0013] Preferably, the contact resistance and contact resistance deviation of the energy meter initially determined to be faulty are calculated, specifically as follows: The sampling voltage of the electricity meter is obtained by dividing the instantaneous measured power of the electricity meter by the product of the actual current of the power grid and the power factor of the power grid. The equivalent resistance of the energy meter is obtained by dividing the difference between the actual grid voltage and the sampled voltage of the energy meter by the actual grid current. The equivalent resistance of the energy meter is fitted to the reciprocal of the sampling current of the energy meter as a linear function, and the contact resistance is obtained by subtracting the set fixed line resistance from the intercept of the linear function. The contact resistance deviation is obtained by multiplying the contact resistance by the actual grid current and dividing by the actual grid voltage.

[0014] The second aspect of this invention proposes an intelligent verification and traceability system for electricity meters based on the method described in the first aspect of this invention, comprising an error measurement module, a preliminary verification module, a degradation model construction module, and a verification and traceability module, specifically as follows: Error measurement module: used to obtain the metering error of the energy meter at each moment under steady-state metering conditions within a set period. The metering error under non-steady-state metering conditions is regarded as a missing value. The missing value is filled in by interpolation. The metering error of the energy meter at each moment after filling in the missing value is a valid error. All valid errors form a valid error sequence, and the excess of the valid error sequence is calculated to form an excess sequence. Preliminary verification module: Extracts modal energy features and power spectrum features from the effective error sequence; inputs the out-of-tolerance sequence, modal energy features and power spectrum features of each energy meter into the pre-trained fault identification algorithm to determine the fault, and outputs the energy meters that are preliminarily determined to be faulty; Degradation model construction module: This module is used to cluster all electricity meters in the power grid according to their model and rated parameter characteristics to obtain different types of electricity meters. For each type of electricity meter, multiple standard electricity meters with the same model characteristics as the electricity meter corresponding to the cluster center are retrieved as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The module fits the mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter. Verification and Traceability Module: This module takes the current operating parameters of the energy meter initially identified as faulty and inputs them into the corresponding mapping function to obtain the theoretical inherent drift; it calculates the contact resistance and contact resistance deviation of the energy meter initially identified as faulty; if the contact resistance is greater than the set resistance threshold, it determines that there is a loose connection between the energy meter's terminals and the wires; it subtracts the theoretical inherent drift and then the contact resistance deviation from the effective error of the energy meter initially identified as faulty to obtain the residual; if the absolute value of the residual exceeds the set residual threshold, it is finally determined to be faulty.

[0015] A third aspect of the invention provides an apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor performs the steps of the intelligent verification and traceability method for electricity meters described in the first aspect of the invention.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, uses the steps of the intelligent verification and traceability method for electricity meters described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: Compared with the prior art, this invention prevents the impact of power grid disturbance noise on the fault detection of electricity meters by screening steady-state metering conditions and completing non-steady-state data interpolation, thereby reducing the false alarm rate; it combines the modal energy and power spectrum features extracted by VMD decomposition for preliminary fault judgment and extracts deep time series features, thereby improving the robustness and sensitivity of fault identification; by decoupling the inherent drift mapping and contact resistance deviation of the same cluster of standard meters, it accurately distinguishes between internal meter faults and external wiring and inherent drift, eliminating interference factors that do not require meter replacement, and only judging internal faults when the residual exceeds the standard, avoiding unnecessary replacement due to environmental drift or loose terminals, and optimizing the accurate allocation of operation and maintenance resources. Attached Figure Description

[0018] Figure 1 This is a system framework diagram of the present invention; Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0020] like Figure 2 As shown, Embodiment 1 of the present invention proposes a smart verification and traceability method for electricity meters, comprising: The metering error of the energy meter at each moment under steady-state metering conditions within a set period is obtained. The metering error under non-steady-state metering conditions is regarded as a missing value. The missing value is filled in by interpolation. The metering error of the energy meter at each moment after filling in the missing value is a valid error. All valid errors form a valid error sequence. The excess of the valid error sequence is calculated to form an excess sequence. Modal energy features and power spectrum features are extracted from the effective error sequence; the out-of-error sequence, modal energy features and power spectrum features of each energy meter are input into the pre-trained fault identification algorithm for fault judgment, and the energy meters that are initially judged to be faulty are output. It should be noted that the fault identification algorithm in this embodiment uses CNN-LSTM, and the training data is a historical steady-state error dataset of electricity meters. The dataset includes normal samples and fault samples. Normal samples are the out-of-range sequences, modal energy features, and power spectrum features of electricity meters that are not faulty under steady-state conditions within a set period (labeled 0). Fault samples are the out-of-range sequences, modal energy features, and power spectrum features of electricity meters that are faulty under steady-state conditions within a set period (labeled 1). The algorithm outputs the probability of each electricity meter being faulty. If the probability exceeds the set probability threshold, the corresponding electricity meter is preliminarily judged to be faulty.

[0021] In addition, under steady-state metering conditions, the total metering error is the inherent drift plus the contact resistance deviation, plus the relatively small amount of stable random noise / white noise. If there is a fault, the deviation caused by the fault is also added. The faulty energy meters initially screened by the fault identification algorithm may be due to inherent drift or loose terminals caused by long service time or environmental factors. These problems do not require replacement of the energy meter. Therefore, further judgment is needed to confirm whether it is an internal fault of the energy meter. If so, the faulty energy meter is repaired or replaced.

[0022] All electricity meters in the power grid are clustered according to their model and rated parameter characteristics to obtain different types of electricity meters. For each type of electricity meter, multiple standard electricity meters with the same model characteristics as the electricity meter corresponding to the cluster center are selected as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter is fitted. It should be noted that the error measured when using a standard electricity meter is the error under conditions of no contact resistance (no looseness between the electricity meter terminals and the wires) and no faults. This error is the inherent drift.

[0023] Substitute the current operating parameters of the energy meter initially identified as faulty into the corresponding mapping function to obtain the theoretical inherent drift; calculate the contact resistance and contact resistance deviation of the energy meter initially identified as faulty; if the contact resistance is greater than the set resistance threshold, it is determined that there is a loose connection between the energy meter terminals and the wires. The effective error of the energy meter initially judged to be faulty is subtracted from the theoretical inherent drift and then from the contact resistance deviation to obtain the residual. If the absolute value of the residual exceeds the set residual threshold, it is finally judged as faulty. Obtaining the faulty energy meter is the way to trace the source of the fault.

[0024] It should be noted that for electricity meters whose faults have not been identified, the theoretical inherent drift and contact resistance deviation are stored in the cloud. Data encryption or identity authentication is required when uploading and reading the data. Subsequently, the data collected by the electricity meter can be corrected based on the theoretical inherent drift and contact resistance deviation.

[0025] In this preferred embodiment, the metering error of the energy meter at each moment under steady-state metering conditions within a set period is obtained, specifically as follows: Obtain the actual voltage, actual current, and power factor of the power grid, and multiply the actual voltage, actual current, and power factor of the power grid to obtain the reference active power; Obtain the instantaneous measured power of the electricity meter, and divide the difference between the instantaneous measured power and the reference active power by the reference active power to obtain the measurement error; The metering error of the electricity meter at each moment under steady-state metering conditions is selected. The metering error under non-steady-state metering conditions is regarded as a missing value and the missing value is filled by interpolation.

[0026] It should be noted that under non-steady-state metering conditions (load switching, motor starting, sudden changes in distributed power output, grid faults and short circuits, etc.), a large amount of noise completely unrelated to whether the meter is faulty is mixed in with the error; in this case, it will be regarded as a sudden increase in meter error and judged as a fault, which is an invalid criterion.

[0027] Interpolation can be done using methods such as linear interpolation, cubic spline interpolation, or Gaussian process regression interpolation.

[0028] In this preferred embodiment, the steady-state metering condition specifically refers to: The absolute value of the load change rate is lower than the set load change threshold; and the absolute value of the ambient temperature change rate is lower than the set environmental parameter change threshold; and the harmonic distortion rate of the actual voltage or current of the power grid is less than the corresponding set harmonic distortion rate threshold; and the actual current of the power grid is greater than the set proportion of the rated current. If the ratio of the total number of times in steady-state metering condition within a set period to the total number of times within a set period is less than the set ratio threshold, it indicates that the current period is an unstable window, and no fault judgment will be performed in the current period.

[0029] Specifically, in this embodiment, the load change rate is the relative change rate, which is the load power at the current moment minus the load power before the set sampling period, and the difference is divided by the change value divided by the load power before the sampling period. The load change threshold is 3%. The temperature change is the temperature change within the sampling period divided by the sampling period. The environmental parameter change threshold is 0.1 K / min. The set ratio is set to 0.1. The ratio threshold is set to 0.8.

[0030] In this preferred embodiment, the calculation of the excess sequence of the effective error sequence is specifically as follows: Determine whether the effective error of the electricity meter at each moment is within the allowable error range corresponding to the accuracy class of the electricity meter. If it is, the error at the corresponding moment is 1; otherwise, it is 0.

[0031] In this preferred embodiment, modal energy features and power spectrum features are extracted from the effective error sequence, specifically as follows: Variational Mode Decomposition (VMD) is performed on the effective error sequence to obtain the Individual Mode Format (IMF) of each mode sequence. The sum of the squared amplitudes of each IMF at each time step is calculated to obtain the energy of each IMF. The energy of each IMF is divided by the sum of the energies of all IMFs to obtain the energy ratio of each IMF as the mode energy feature. Each mode sequence IMF is converted into a power spectrum sequence using Fourier transform. For each mode sequence IMF, the center frequency of the corresponding mode sequence IMF is subtracted from the frequency corresponding to the m-th sampling point. The square of the difference is multiplied by the value corresponding to the m-th sampling point in the power spectrum sequence to obtain the frequency deviation weighted power of the m-th sampling point. The frequency deviation weighted power of all sampling points within the set period is superimposed and divided by the sum of the values ​​corresponding to all sampling points in the power spectrum sequence. The square root of the division result is used as the average spectral radius as the power spectrum feature.

[0032] The average spectral radius of the IMF of the k-th modal sequence The formula is:

[0033] in, To set the total number of sampling points (total time points) within a set period; This represents the frequency value corresponding to the m-th sampling point; The center frequency of the IMF of the k-th modal sequence; Let be the power spectral density value of the k-th mode at the m-th frequency point.

[0034] In this preferred embodiment, all electricity meters in the power grid are clustered according to their model and rated parameter characteristics to obtain different types of electricity meters. Specifically: Model characteristics include: nameplate code and manufacturer; rated parameter characteristics include: accuracy class, basic current, median of rated operating temperature range, mean error under steady load, and standard deviation of steady load error; Clustering uses the K-medoids algorithm; during clustering, if two energy meters have the same nameplate code, the nameplate code matching cost is 0, otherwise it is a set value; if two energy meters have the same manufacturer, the manufacturer matching cost is 0, otherwise it is a set value; the set value is less than 1. After normalizing the rated parameter features, a rated parameter feature vector is formed. The square of the difference between the norms of the rated parameter feature vectors of two energy meters is calculated, plus the square of the nameplate code matching cost, plus the square of the manufacturer matching cost. The square root of the sum is used to obtain the cluster distance.

[0035] It should be noted that the normalization of rated parameter characteristics involves setting a reference value for each rated parameter characteristic and then dividing each rated parameter characteristic by its corresponding reference value. The accuracy class of an electricity meter is usually expressed numerically, such as 0.2, 0.5, 1.0, and 2.0, with a reference value of 1. The smaller the number, the higher the measurement accuracy. If the value is not expressed numerically, it is converted to a numerical value before normalization.

[0036] In this preferred embodiment, the mapping function between the operating parameters and fixed drift of the corresponding type of electricity meter is fitted, specifically as follows: Operating parameters include load, ambient temperature, and usage time; these parameters are combined into an operating parameter vector. A set number of kernel basis functions are weighted and superimposed, and the superposition result is added with a global bias value to obtain a fixed drift; the bandwidth parameter, center coordinates and weights of each kernel basis function are all fitted parameters; The center coordinates are a vector composed of the reference values ​​of the load, the ambient temperature, and the usage time. The kernel basis function is calculated by multiplying the square of the norm of the running parameter vector and its corresponding center coordinates, multiplying it by the corresponding bandwidth parameter, and then multiplying it by -1 as the exponent. The natural base e is used as the base for the exponentiation operation, and the result is the kernel basis function.

[0037] Mapping function The formula is:

[0038] in, , , These represent the load, ambient temperature, and usage time, respectively. This is the global bias value; This represents the total number of kernel basis functions; Let j be the weight of the j-th kernel basis function. For the runtime parameter vector; The coordinates of the center of the j-th kernel basis function are given; T is the transpose sign. Let be the bandwidth parameter of the j-th kernel basis function.

[0039] In this preferred embodiment, the contact resistance and contact resistance deviation of the energy meter initially determined to be faulty are calculated, specifically as follows: The sampling voltage of the electricity meter is obtained by dividing the instantaneous measured power of the electricity meter by the product of the actual current of the power grid and the power factor of the power grid. The equivalent resistance of the energy meter is obtained by dividing the difference between the actual grid voltage and the sampled voltage of the energy meter by the actual grid current. The equivalent resistance of the energy meter is fitted to the reciprocal of the sampling current of the energy meter as a linear function, and the contact resistance is obtained by subtracting the set fixed line resistance from the intercept of the linear function. The contact resistance deviation is obtained by multiplying the contact resistance by the actual grid current and dividing by the actual grid voltage.

[0040] It should be noted that contact resistance, which is the additional resistance between the electricity meter's terminals and the wires due to looseness, can cause the voltage sampled internally by the electricity meter to be lower than the actual grid voltage, thus resulting in measurement errors. The edge node directly measures the actual voltage, current, and power factor on the grid side using high-precision sensors. Simultaneously, it calculates the instantaneous power measured by the meter under test using the pulse signal output from the meter. By dividing the power of the meter under test by the actual current and power factor measured by the edge node, the actual voltage value sampled internally by the meter can be deduced. The difference between the actual voltage measured at the edge node and this inversely calculated voltage, divided by the actual current, is the equivalent resistance of the energy meter. The equivalent resistance includes the fixed line resistance (obtainable from the energy meter manual or through simulation and experiment), the contact resistance, and the equivalent resistance due to voltage drop caused by aging. However, the equivalent resistance due to voltage drop caused by aging is an equivalent resistance, not the actual resistance, and the voltage drop caused by aging is only related to the degree of aging and does not change with the current. On the other hand, the contact resistance and the fixed line resistance (or the internal resistance of the energy meter) both change with the current. Therefore, the equivalent resistance of the energy meter is fitted to the reciprocal of the energy meter's sampling current as a linear function, and the intercept of the linear function is the contact resistance plus the fixed line resistance.

[0041] The deviation of the meter's sampling voltage caused by the contact resistance is obtained by multiplying the contact resistance by the actual grid current. The deviation of the meter's sampling voltage caused by the contact resistance divided by the actual grid voltage equals the power deviation of the meter caused by the contact resistance divided by the reference power, which is the contact resistance deviation.

[0042] like Figure 1 As shown, Embodiment 2 of the present invention proposes an intelligent verification and traceability system for electricity meters based on the method described in Embodiment 1 of the present invention, including an error measurement module, a preliminary verification module, a degradation model construction module, and a verification and traceability module, specifically as follows: Error measurement module: used to obtain the metering error of the energy meter at each moment under steady-state metering conditions within a set period. The metering error under non-steady-state metering conditions is regarded as a missing value. The missing value is filled in by interpolation. The metering error of the energy meter at each moment after filling in the missing value is a valid error. All valid errors form a valid error sequence, and the excess of the valid error sequence is calculated to form an excess sequence. Preliminary verification module: Extracts modal energy features and power spectrum features from the effective error sequence; inputs the out-of-tolerance sequence, modal energy features and power spectrum features of each energy meter into the pre-trained fault identification algorithm to determine the fault, and outputs the energy meters that are preliminarily determined to be faulty; Degradation model construction module: This module is used to cluster all electricity meters in the power grid according to their model and rated parameter characteristics to obtain different types of electricity meters. For each type of electricity meter, multiple standard electricity meters with the same model characteristics as the electricity meter corresponding to the cluster center are retrieved as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The module fits the mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter. Verification and Traceability Module: This module takes the current operating parameters of the energy meter initially identified as faulty and inputs them into the corresponding mapping function to obtain the theoretical inherent drift; it calculates the contact resistance and contact resistance deviation of the energy meter initially identified as faulty; if the contact resistance is greater than the set resistance threshold, it determines that there is a loose connection between the energy meter's terminals and the wires; it subtracts the theoretical inherent drift and then the contact resistance deviation from the effective error of the energy meter initially identified as faulty to obtain the residual; if the absolute value of the residual exceeds the set residual threshold, it is finally determined to be faulty.

[0043] In this preferred embodiment, the intelligent verification and traceability system further includes a cloud calibration module, which includes a calibration unit and an encrypted evidence storage unit. The cloud calibration module calibrates the energy meter using theoretically inherent drift and contact resistance deviation. The encrypted evidence storage unit is used for data encryption or identity authentication during data upload and retrieval.

[0044] Among them, the error measurement module, the preliminary verification module, and the verification traceability module are located on the edge side and arranged near the electricity meter cluster.

[0045] In this preferred embodiment, the error measurement module includes an acquisition module, a data completion module, and an out-of-tolerance detection module. The acquisition module acquires the metering data of the energy meter at each moment under steady-state metering conditions within a set period through a pulse input interface, and measures the actual voltage, current, power, and phase on the grid side through a high-precision sensor. All acquired signals are filtered. The data completion module calculates the power measurement error based on the metering data, and treats the metering error under non-steady-state metering conditions as a missing value, completing the missing value through interpolation. The metering error of the energy meter at each moment after completion is a valid error, and all valid errors form a valid error sequence. The out-of-tolerance detection module is used to calculate the out-of-tolerance sequence of the valid error sequence. The preliminary verification module includes a feature extraction module and a fault algorithm judgment module. The feature extraction module extracts modal energy features and power spectrum features from the effective error sequence. The fault algorithm judgment module inputs the out-of-range sequence, modal energy features and power spectrum features of each energy meter into the pre-trained fault identification algorithm to judge the fault and outputs the energy meters that are initially judged to be faulty. The degradation model construction module includes a trend classification module and a function fitting module. The trend classification module is used to cluster all electricity meters in the power grid according to their model and rated parameter characteristics to obtain different types of electricity meters. The function fitting module is used to retrieve multiple standard electricity meters with the same model characteristics as the electricity meters corresponding to the cluster center for each type of electricity meter as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The module fits the mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter. The verification and traceability module includes a theoretical inherent drift calculation module, a contact resistance deviation calculation module, and a final fault judgment module. The theoretical inherent drift calculation module is used to input the current operating parameters of the energy meter initially judged to be faulty into the corresponding mapping function to obtain the theoretical inherent drift. The contact resistance deviation calculation module is used to calculate the contact resistance and contact resistance deviation of the energy meter initially judged to be faulty. If the contact resistance is greater than the set resistance threshold, it is determined that there is a loose connection between the energy meter's terminals and the wires. The final fault judgment module is used to subtract the theoretical inherent drift and the contact resistance deviation from the effective error of the energy meter initially judged to be faulty to obtain the residual. If the absolute value of the residual exceeds the set residual threshold, it is finally judged to be faulty.

[0046] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0047] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0048] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0049] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A smart verification and traceability method for electricity meters, characterized in that, include: The metering error of the energy meter at each moment under steady-state metering conditions within a set period is obtained. The metering error under non-steady-state metering conditions is regarded as a missing value. The missing value is filled in by interpolation. The metering error of the energy meter at each moment after filling in the missing value is a valid error. All valid errors form a valid error sequence. The excess of the valid error sequence is calculated to form an excess sequence. Extract modal energy features and power spectrum features from the effective error sequence; The out-of-tolerance sequence, modal energy characteristics, and power spectrum characteristics of each energy meter are input into a pre-trained fault identification algorithm to determine the fault and output the energy meters that are initially determined to be faulty. All electricity meters in the power grid are clustered according to their model and rated parameter characteristics to obtain different types of electricity meters. For each type of electricity meter, multiple standard electricity meters with the same model characteristics as the electricity meter corresponding to the cluster center are selected as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter is fitted. Substitute the current operating parameters of the energy meter initially identified as faulty into the corresponding mapping function to obtain the theoretical inherent drift. Calculate the contact resistance and contact resistance deviation of the energy meter initially identified as faulty; if the contact resistance is greater than the set resistance threshold, it is determined that there is a loose connection between the energy meter terminals and the wires. The effective error of the energy meter initially judged to be faulty is subtracted from the theoretical inherent drift and then from the contact resistance deviation to obtain the residual. If the absolute value of the residual exceeds the set residual threshold, it is finally judged to be faulty.

2. The intelligent verification and traceability method for electricity meters according to claim 1, characterized in that: To obtain the metering error of the energy meter at each moment under steady-state metering conditions within a set period, specifically: Obtain the actual voltage, actual current, and power factor of the power grid, and multiply the actual voltage, actual current, and power factor of the power grid to obtain the reference active power; Obtain the instantaneous measured power of the electricity meter, and divide the difference between the instantaneous measured power and the reference active power by the reference active power to obtain the metering error; The metering error of the electricity meter at each moment under steady-state metering conditions is selected. The metering error under non-steady-state metering conditions is regarded as a missing value and the missing value is filled by interpolation.

3. The intelligent verification and traceability method for electricity meters according to claim 2, characterized in that: The steady-state metering condition is specifically as follows: The absolute value of the load change rate is lower than the set load change threshold; and the absolute value of the ambient temperature change rate is lower than the set environmental parameter change threshold; and the harmonic distortion rate of the actual voltage or current of the power grid is less than the corresponding set harmonic distortion rate threshold; and the actual current of the power grid is greater than the set proportion of the rated current. If the ratio of the total number of times in steady-state metering condition within a set period to the total number of times within a set period is less than the set ratio threshold, it indicates that the current period is an unstable window, and no fault judgment will be performed in the current period.

4. The intelligent verification and traceability method for electricity meters according to claim 1, characterized in that: The excess sequence is calculated by composing the excess sequence from the excess of the effective error sequence. Determine whether the effective error of the electricity meter at each moment is within the allowable error range corresponding to the accuracy class of the electricity meter. If it is, the error at the corresponding moment is 1; otherwise, it is 0.

5. The intelligent verification and traceability method for electricity meters according to claim 1, characterized in that: Modal energy features and power spectrum features are extracted from the effective error sequence, specifically as follows: Variational Mode Decomposition (VMD) is performed on the effective error sequence to obtain the Individual Mode Format (IMF) of each mode sequence. The sum of the squared amplitudes of each IMF at each time step is calculated to obtain the energy of each IMF. The energy of each IMF is divided by the sum of the energies of all IMFs to obtain the energy ratio of each IMF as the mode energy feature. Each mode sequence IMF is converted into a power spectrum sequence using Fourier transform. For each mode sequence IMF, the center frequency of the corresponding mode sequence IMF is subtracted from the frequency corresponding to the m-th sampling point. The square of the difference is multiplied by the value corresponding to the m-th sampling point in the power spectrum sequence to obtain the frequency deviation weighted power of the m-th sampling point. The frequency deviation weighted power of all sampling points within the set period is superimposed and divided by the sum of the values ​​corresponding to all sampling points in the power spectrum sequence. The square root of the division result is used as the average spectral radius as the power spectrum feature.

6. The intelligent verification and traceability method for electricity meters according to claim 1, characterized in that: All electricity meters in the power grid are clustered according to their model and rated parameter characteristics to obtain different types of electricity meters, specifically: Model characteristics include: nameplate code and manufacturer; rated parameter characteristics include: accuracy class, basic current, median of rated operating temperature range, mean error under steady load, and standard deviation of steady load error; Clustering uses the K-medoids algorithm; during clustering, if two energy meters have the same nameplate code, the nameplate code matching cost is 0, otherwise it is a set value; if two energy meters have the same manufacturer, the manufacturer matching cost is 0, otherwise it is a set value; the set value is less than 1. After normalizing the rated parameter features, a rated parameter feature vector is formed. The square of the difference between the norms of the rated parameter feature vectors of two energy meters is calculated, plus the square of the nameplate code matching cost, plus the square of the manufacturer matching cost. The square root of the sum is used to obtain the cluster distance.

7. The intelligent verification and traceability method for electricity meters according to claim 1, characterized in that: The mapping function between the operating parameters and fixed drift of the corresponding type of electricity meter is fitted as follows: Operating parameters include load, ambient temperature, and usage time; these parameters are combined into an operating parameter vector. A set number of kernel basis functions are weighted and superimposed, and the superposition result is added with a global bias value to obtain a fixed drift; the bandwidth parameter, center coordinates and weights of each kernel basis function are all fitted parameters; The center coordinates are a vector composed of the reference values ​​of the load, the ambient temperature, and the usage time. The kernel basis function is calculated by multiplying the square of the norm of the running parameter vector and its corresponding center coordinates, multiplying it by the corresponding bandwidth parameter, and then multiplying it by -1 as the exponent. The natural base e is used as the base for the exponentiation operation, and the result is the kernel basis function.

8. The intelligent verification and traceability method for electricity meters according to claim 1, characterized in that: The contact resistance and contact resistance deviation of the energy meter initially identified as faulty are calculated as follows: The sampling voltage of the electricity meter is obtained by dividing the instantaneous measured power of the electricity meter by the product of the actual current of the power grid and the power factor of the power grid. The equivalent resistance of the energy meter is obtained by dividing the difference between the actual grid voltage and the sampled voltage of the energy meter by the actual grid current. The equivalent resistance of the energy meter is fitted to the reciprocal of the sampling current of the energy meter as a linear function, and the contact resistance is obtained by subtracting the set fixed line resistance from the intercept of the linear function. The contact resistance deviation is obtained by multiplying the contact resistance by the actual grid current and dividing by the actual grid voltage.

9. A smart verification and traceability system for electricity meters based on the method of any one of claims 1-8, comprising an error measurement module, a preliminary verification module, a degradation model construction module, and a verification and traceability module, characterized in that: Error measurement module: used to obtain the metering error of the energy meter at each moment under steady-state metering conditions within a set period. The metering error under non-steady-state metering conditions is regarded as a missing value. The missing value is filled in by interpolation. The metering error of the energy meter at each moment after filling in the missing value is a valid error. All valid errors form a valid error sequence, and the excess of the valid error sequence is calculated to form an excess sequence. Preliminary verification module: Extracts modal energy features and power spectrum features from the effective error sequence; The out-of-tolerance sequence, modal energy characteristics, and power spectrum characteristics of each energy meter are input into a pre-trained fault identification algorithm to determine the fault and output the energy meters that are initially determined to be faulty. Degradation model construction module: This module is used to cluster all electricity meters in the power grid according to their model and rated parameter characteristics to obtain different types of electricity meters. For each type of electricity meter, multiple standard electricity meters with the same model characteristics as the electricity meter corresponding to the cluster center are retrieved as reference electricity meters. By testing the effective error of each reference electricity meter under different operating parameters, the effective error of the standard electricity meter is a fixed drift. The module fits the mapping function between the operating parameters and the fixed drift of the corresponding type of electricity meter. Verification and tracing module: used to input the current operating parameters of the energy meter that is initially judged to be faulty into the corresponding mapping function to obtain the theoretical inherent drift; Calculate the contact resistance and contact resistance deviation of the energy meter initially identified as faulty; if the contact resistance is greater than the set resistance threshold, it is determined that there is a loose connection between the energy meter terminals and the wires. The effective error of a preliminarily determined faulty energy meter is reduced by the theoretical inherent drift and then by the contact resistance deviation to obtain the residual. If the absolute value of the residual exceeds the set residual threshold, it is finally determined to be faulty.

10. An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor performs the steps of using the intelligent verification and traceability method for electricity meters as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, uses the steps of the intelligent verification and traceability method for electricity meters as described in any one of claims 1 to 8.

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