Intelligent calibration method and system for electric energy meter

Abstract

The invention relates to the technical field of electric energy meter calibration, and provides an intelligent calibration method and system for an electric energy meter. The method comprises the steps that after calibration is triggered, a bus electric energy meter is switched to a bus-level electrical isolation scene through a switching relay; extracting a first group of bus-level metering values after injecting all-condition steady-state signals; comparing the theoretical values to calculate a first group of bus-level errors; judging whether to switch to a branch-level electrical isolation scene or not according to the error distribution characteristics; if switching is carried out, a multi-dimensional disturbance load combination is dynamically loaded on the K branch circuits through the program control load module, and a second group of bus level metering values are synchronously metered; and performing branch-level energy consumption error traceability calculation on the second group of values, and executing closed-loop calibration to solve the technical problems of low calibration precision and incapability of accurately reflecting error distribution caused by incapability of coping with a multi-dimensional disturbance load environment in real time in a traditional electric energy meter calibration process, so as to achieve the purposes of improving the accurate calibration capability of the electric energy meter in multiple scenes and improving the calibration accuracy of the electric energy meter in multiple scenes. And the equipment calibration time is shortened.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy meter calibration, and in particular to an intelligent calibration method and system for electric energy meters. Background Art

[0002] As an important device for electricity metering and management, electric energy meters are widely used in electricity metering, load management, and intelligent billing. Traditional calibration is usually performed under a single steady-state load, which makes it difficult to deal with the problem of accumulated metering deviations caused by dynamic disturbances in multiple branch circuits (such as harmonic pollution, sudden load changes, and backfeed of distributed power sources) during actual operation. Existing technologies lack the ability to trace the interactive effects of branch-level energy consumption and rely on manual offline calibration, which causes systematic error drift in electric energy meters in real scenarios. Especially in industrial and commercial multi-branch power supply systems, the metering accuracy of bus electric energy meters is affected by the nonlinear load coupling of branch circuits. Existing test equipment cannot simultaneously simulate dynamic load combinations and electrical isolation scenarios, making the calibration results disconnected from actual operation. Summary of the Invention

[0003] This application provides an intelligent calibration method and system for electricity meters, aiming to solve the technical problems that traditional electricity meters cannot respond to multi-dimensional disturbance load environments in real time during calibration, resulting in low calibration accuracy and inaccurate reflection of error distribution in complex scenarios. The application achieves the technical effect of improving the accurate calibration capability of electricity meters in multiple scenarios and reducing equipment calibration time through staged electrical isolation and dynamic multi-dimensional disturbance load loading.

[0004] The first aspect disclosed in the present application provides an intelligent calibration method for an electric energy meter, the method comprising: after calibration is triggered, switching the bus electric energy meter to a bus-level electrical isolation scenario by switching a relay; in the bus-level electrical isolation scenario, injecting a full-operating-condition steady-state signal strategy into the bus electric energy meter, performing metering data extraction, and outputting a first group of bus-level metering values; performing test error calculation by comparing the first group of bus-level metering values with the full-operating-condition theoretical values, and outputting a first group of bus-level errors; making a scenario switching judgment based on the error distribution characteristics of the first group of bus-level errors, and if a switching is judged to be performed, switching the bus electric energy meter to a branch-level electrical isolation scenario by switching the relay; in the branch-level electrical isolation scenario, synchronously measuring a second group of bus-level metering values of the bus electric energy meter during the process of dynamically loading a multi-dimensional disturbance load combination in K branch circuits through a programmable load module; performing branch-level energy consumption error tracing calculation on the second group of bus-level metering values, and performing closed-loop calibration on the bus electric energy meter based on the calculation results.

[0005] Another aspect disclosed in this application provides an intelligent calibration system for an electric energy meter. The system includes: a calibration trigger unit: after calibration is triggered, the bus electric energy meter is switched to a bus-level electrical isolation scenario through a switching relay; a first measurement data extraction unit: in the bus-level electrical isolation scenario, after injecting a full-condition steady-state signal strategy into the bus electric energy meter, measurement data is extracted and the first set of bus-level measurement values is output; a test error calculation unit: the first set of bus-level measurement values is used to compare with the full-condition theoretical values for test error calculation, and the first set of bus-level errors is output; a scenario switching judgment unit: based on the error distribution characteristics of the first set of bus-level errors, scenario switching is judged. If it is judged to switch, the bus electric energy meter is switched to a branch-level electrical isolation scenario through the switching relay; a second measurement data extraction unit: in the branch-level electrical isolation scenario, during the dynamic loading of a multi-dimensional disturbance load combination on K branch circuits by a programmable load module, the second set of bus-level measurement values of the bus electric energy meter is synchronously measured; a closed-loop calibration unit: performs branch-level energy consumption error traceability calculation on the second set of bus-level measurement values, and performs closed-loop calibration on the bus electric energy meter according to the calculation results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above intelligent calibration method for an electric energy meter realizes automatic calibration of the electric energy meter through intelligent control. After calibration is started, first, the electric energy meter is switched to a bus-level electrical isolation scenario through a relay. Subsequently, a full-condition steady-state signal strategy is injected into the electric energy meter, data is extracted, and the first set of bus-level measurement values is obtained. Then, by comparing with the full-condition theoretical values, the test error is calculated, and the first set of error data is output. Then, based on the error distribution characteristics, it is judged whether it is necessary to switch to a branch-level electrical isolation scenario. If it is necessary to switch, the electric energy meter is switched to a branch-level electrical isolation scenario through a relay. In this scenario, a multi-dimensional disturbance load is loaded through a programmable load module, and the second set of bus-level measurement values is synchronously measured. Finally, based on the second set of measurement values, energy consumption error traceability calculation is performed, and finally closed-loop calibration is performed to ensure the high precision and high stability of the electric energy meter.

[0007] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. Description of the Drawings

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0009] Figure 1 It is a schematic flowchart of an intelligent calibration method for an electric energy meter in an embodiment.

[0010] Figure 2 It is an architecture diagram of an intelligent calibration system for an electric energy meter in an embodiment.

[0011] Explanation of reference numerals: calibration trigger unit 11, first measurement data extraction unit 12, test error calculation unit 13, scenario switching judgment unit 14, second measurement data extraction unit 15, closed-loop calibration unit 16. Specific implementation manners

[0012] By providing an intelligent calibration method and system for an electric energy meter in the embodiments of the present application, the technical problems that in the traditional calibration process of an electric energy meter, it is impossible to respond to a multi-dimensional disturbance load environment in real time, resulting in low calibration accuracy and the error distribution in complex scenarios cannot be accurately reflected are solved. The technical effect of improving the accurate calibration ability of the electric energy meter in multiple scenarios and reducing the equipment calibration time is achieved through staged electrical isolation and dynamic multi-dimensional disturbance load loading.

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0014] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 shown, the present application provides an intelligent calibration method for an electric energy meter, and the method includes: After calibration is triggered, the bus electric energy meter is switched to the bus-level electrical isolation scenario through a switching relay.

[0016] In the embodiment of the present application, after calibration is started, first through the operation of the relay, the bus electricity meter is switched from the current operating state to an electrically isolated scenario. This process ensures that the bus electricity meter is disconnected from other circuits, thereby avoiding external interference and providing a clean and independent calibration environment. In this isolated state, the electricity meter can accurately collect measurement data and perform calibration without external influence, ensuring the accuracy of the calibration result.

[0017] Furthermore, the present application provides that after calibration is triggered, the bus electricity meter is switched to a bus-level electrical isolation scenario by switching the relay. The method includes: The switching relay performs a branch load locking operation to electrically disconnect the bus electricity meter from K branch circuits; after the switching relay performs a grid input cut-off operation, the bus electricity meter is connected to a standard source, where the standard source is used to inject the full-condition steady-state signal strategy into the bus electricity meter.

[0018] Preferably, after calibration is triggered, the switching relay first performs a branch load locking operation, which means it disconnects the bus electricity meter from K branch circuits, thereby completely isolating the electricity meter from these branch circuits and ensuring that the calibration process is not interfered by external load changes. Subsequently, the switching relay performs a grid input cut-off operation to disconnect the bus electricity meter from the grid. The purpose of this step is to eliminate the influence of the grid on the electricity meter and provide a stable environment for subsequent precise calibration. After that, the switching relay connects the bus electricity meter to a standard source. The standard source provides a known and stable power signal, which is used to inject the full-condition steady-state signal strategy into the bus electricity meter. This steady-state signal strategy includes working condition information such as different voltages, currents, and power factors, so as to simulate the operating state of the electricity meter under various load conditions and ensure that the electricity meter can be accurately calibrated under all working conditions.

[0019] Table 1: Example Table of Full-Condition Steady-State Signal Strategy

[0020] As shown in Table 1, the example table of the full-condition steady-state signal strategy covers typical working conditions that the electricity meter may encounter, including different voltage levels, load levels, load characteristics, and power factor combinations, and is used to verify the measurement accuracy and stability of the electricity meter under all working conditions.

[0021] Furthermore, the present application provides that before the bus electricity meter is switched to a bus-level electrical isolation scenario by switching the relay after calibration is triggered, the method includes: Locally call the multi-source historical status data of the bus electric energy meter; after extracting the multi-scale steady-state operation duration from the multi-source historical status data, compare and extract the minimum value to generate a timing trigger threshold; calculate the standard deviation of the active power of the multi-source historical status data through a sliding window to generate a first event trigger threshold; calculate the range ratio of the effective value of the branch circuit current based on the multi-source historical status data to generate a second event trigger threshold; input the timing trigger threshold, the first event trigger threshold, and the second event trigger threshold into the state machine to construct a calibration trigger condition matching engine.

[0022] Preferably, first, obtain the multi-source historical status data related to the bus electric energy meter through local calling. These data come from different monitoring sources and contain the operation status information of the electric energy meter in different time periods, such as voltage, current, power, etc. Subsequently, from the obtained multi-source historical status data, extract the multi-scale steady-state operation duration data, which reflect the duration of the electric energy meter in a stable working state. By comparing these duration data, find the minimum value, and generate a timing trigger threshold based on this minimum value as the starting time point of the subsequent calibration process. Then, according to the preset time window size, use a sliding window to calculate the standard deviation of the active power in the multi-source historical status data. The standard deviation reflects the fluctuation amplitude of the power data. Through the calculation of the standard deviation, generate a first event trigger threshold, which is used to judge whether the power fluctuation exceeds the predetermined standard, thereby triggering a calibration event. In addition, based on the multi-source historical status data, calculate the range ratio of the effective value of the branch circuit current (i.e., the ratio of the current range to the current mean value) to reflect the amplitude of the current change. Through the calculation of the range ratio, generate a second event trigger threshold, which is used to judge whether the current fluctuation exceeds the predetermined standard, thereby triggering relevant calibration operations. Finally, input the above-generated timing trigger threshold, the first event trigger threshold, and the second event trigger threshold into the state machine. The state machine makes logical judgments based on these threshold conditions to construct a calibration trigger condition matching engine. When one or more of the conditions are met, the calibration process will be triggered to ensure that the electric energy meter enters the calibration state at the appropriate time. Through this process, the automation and precise triggering of the electric energy meter calibration can be realized, improving the reliability and real-time performance of the calibration.

[0023] In the bus-level electrical isolation scenario, after injecting the full-condition steady-state signal strategy into the bus electric energy meter, perform metering data extraction and output the first group of bus-level metering values.

[0024] In one embodiment, in the scenario of bus-level electrical isolation, first, a full-condition steady-state signal strategy is injected into the bus watt-hour meter through a standard source. This signal strategy includes parameters such as voltage, current, and power factor of the watt-hour meter under various different conditions, ensuring that the watt-hour meter is in a stable operating state. After this signal is injected, the watt-hour meter starts to perform metering operations based on these input signals. At this time, a metering data extraction operation will be executed to read the metering data generated by the watt-hour meter. These data include values such as voltage, current, and power measured by the watt-hour meter. After being processed, the first set of bus-level metering values is output. These metering values serve as the basis for the calibration process and provide the actual operating data of the watt-hour meter under specific conditions.

[0025] Furthermore, after injecting the full-condition steady-state signal strategy into the bus watt-hour meter in the scenario of bus-level electrical isolation, this application provides a method for performing metering data extraction and outputting the first set of bus-level metering values, which includes: Match the full-condition strategy information according to the application scenario of the bus watt-hour meter, where the full-condition strategy information is composed of a rated voltage condition sequence, a current condition range grading, and a rated power factor sequence; after randomly disturbing the full-condition strategy information by interacting with the multi-condition fluctuation scale, construct multiple sets of steady-state test signals based on orthogonal decomposition; encapsulate the multiple sets of steady-state test signals in a voltage-gradient ascending structure to form a full-condition steady-state signal injection sequence, which serves as the full-condition steady-state signal strategy; during the process of controlling the standard source to inject the full-condition steady-state signal injection sequence into the bus watt-hour meter in discrete time steps, synchronously execute metering data extraction and output the first set of bus-level metering values.

[0026] Optionally, according to the application scenario of the bus electric energy meter, keyword matching is performed on the operating condition descriptions in the full-condition strategy library, and the full-condition strategy information that conforms to the current application scenario is extracted therefrom. These strategy information include the rated sequence of voltage conditions, the range grading of current conditions, and the rated sequence of power factors, which respectively represent the operating states of the electric energy meter under different voltages, different current ranges, and different power factors. Subsequently, random perturbations are applied to each operating condition parameter in the matched full-condition strategy information to simulate the responses of the electric energy meter under different operating conditions, making the test signals closer to various environmental changes that may be encountered in actual operation. Based on the perturbed full-condition strategy information, the orthogonal decomposition method is used to decompose it into multiple groups of independent steady-state test signals. Specifically, first, the perturbed full-condition strategy information is centered, that is, the mean value of each signal is subtracted from each data point of each signal to make the data have zero mean. Then, according to the centered data, the covariance matrix is calculated to reflect the linear relationship and variance between each signal. After that, the covariance matrix is subjected to eigenvalue decomposition to obtain eigenvalues and eigenvectors. The eigenvalues represent the importance of each principal component, and the eigenvectors represent the directions of the principal components. According to the magnitudes of the eigenvalues, the eigenvector corresponding to the largest eigenvalue is selected as the principal component. Usually, the eigenvectors corresponding to the first k largest eigenvalues are selected. By multiplying the centered data matrix by the selected eigenvector matrix, the orthogonal components (i.e., principal components) of each signal are obtained. These orthogonal components are independent of each other and are used as a group of steady-state test signals respectively. These signals reflect the stable operating states of the electric energy meter under different conditions and are helpful for the accurate testing of various indicators of the electric energy meter during the calibration process. Then, the obtained multiple groups of steady-state test signals are structurally encapsulated in ascending order of voltage gradient, that is, these test signals are organized in the order of increasing voltage to form a series of ordered test signal sequences, ensuring the gradualness of signal injection and the stability of the system. After encapsulation, all the steady-state test signals are sequentially formed into a complete full-condition steady-state signal injection sequence, which will be used as the full-condition steady-state signal strategy for the subsequent accurate calibration of the electric energy meter. Finally, using the standard source device in a discrete time sequence and step-by-step control manner, the constructed full-condition steady-state signals are injected into the bus electric energy meter. During the signal injection process, the metering data extraction operation is synchronously executed to read the metering data of the electric energy meter in real time. Through these data, the first group of bus-level metering values are generated, which provide data support for the subsequent error calculation and calibration process.

[0027] The first group of bus-level errors are output by calculating the test errors by comparing the first group of bus-level metering values with the full-condition theoretical values.

[0028] In one embodiment, during the calibration process, first, the obtained first set of bus-level measurement values is compared with the theoretical values under all operating conditions. The theoretical values under all operating conditions are the values that the electricity meter should display based on known ideal operating conditions and are usually calculated according to standard parameters such as voltage, current, and power factor. By comparing the difference between the actual measurement values and the theoretical values, the first set of bus-level errors is obtained. This first set of bus-level errors reflects the performance deviation of the electricity meter under the current operating conditions and provides a basis for subsequent calibration adjustments.

[0029] Based on the error distribution characteristics of the first set of bus-level errors, a scenario switching judgment is made. If it is judged to switch, the bus electricity meter is switched to the branch-level electrical isolation scenario through the switching relay.

[0030] In one embodiment, after the error calculation, the distribution characteristics of the first set of bus-level errors are analyzed to evaluate the manifestation form and degree of the errors. For example, statistical characteristics such as the fluctuation range and standard deviation of the errors are checked. If the distribution characteristics of these errors reach the preset switching standard, it is judged whether a scenario switch is required. This judgment determines whether the bus electricity meter needs to be switched from the current bus-level electrical isolation scenario to the branch-level electrical isolation scenario. If it is judged that a switch is required, the operation is performed through the switching relay to switch the bus electricity meter to the branch-level electrical isolation scenario. This switching operation ensures that the electricity meter can continue to perform precise calibration in the new test environment and avoids the influence of the original errors on the subsequent process.

[0031] Furthermore, the present application provides a method for making a scenario switching judgment based on the error distribution characteristics of the first set of bus-level errors. If it is judged to switch, the bus electricity meter is switched to the branch-level electrical isolation scenario through the switching relay. The method includes: Performing multi-dimensional feature extraction on the first set of bus-level errors based on a preset correlation index, and outputting bus-level error features. Among them, the bus-level error features include bus-level range, bus-level standard deviation, bus-level skewness coefficient, and bus-level proportion exceeding the threshold; determining the error distribution mode based on the bus-level error features, and outputting the distribution mode category; matching the switching decision rule library according to the distribution mode category. If the decision output is a branch-level isolation instruction, driving the switching relay to switch the bus electricity meter to the branch-level electrical isolation scenario.

[0032] Preferably, first, according to the preset correlation index, characteristic data of multiple dimensions are extracted from the first group of bus-level errors to obtain bus-level error characteristics, which include bus-level range, bus-level standard deviation, bus-level skewness coefficient and bus-level over-threshold ratio, wherein the bus-level range is the difference between the maximum and minimum values of the error value, reflecting the overall variation range of the error; the bus-level standard deviation is the average level of the difference between the error value and the error mean, which is used to measure the degree of dispersion of the error data. The larger the standard deviation, the greater the error fluctuation; the bus-level skewness coefficient is obtained by measuring the asymmetry of the error data distribution. If the data is biased to the right (more large error values), the skewness coefficient is positive. If the data is biased to the left (more small error values), the skewness coefficient is negative. If the data is symmetrically distributed, the skewness coefficient is close to zero; the bus-level over-threshold ratio is the proportion of errors that exceed the set error threshold, which helps to determine whether the error is abnormal. Subsequently, based on the extracted bus-level error characteristics, the error distribution pattern will be analyzed and the distribution pattern category will be determined. In this process, by comparing with the known error distribution types, the distribution characteristics of the current error will be judged. For example, if during the calibration process, the bus-level range is 0.1%~0.3%, the bus-level standard deviation is 0.02%~0.08%, the bus-level skewness coefficient is -0.2~+0.2, and the bus-level over-threshold ratio is 0.5%~2%, this may indicate that the error data is ideally normally distributed; if the bus-level range is 0.3%~0.8%, the bus-level standard deviation is 0.08%~0.2%, the bus-level skewness coefficient is -0.5~+0.5, and the bus-level over-threshold ratio is 2%~5%, this may indicate that the error data is conventionally normally distributed. Then, based on the determined error distribution pattern category, a matching switching decision rule is searched from the switching decision rule library. If the error pattern meets the criteria for switching to the branch-level electrical isolation scenario, a branch-level isolation instruction is generated through the switching decision rule library to activate the switching relay, thereby switching the bus energy meter from the current bus-level electrical isolation scenario to the branch-level electrical isolation scenario. This switch ensures that the energy meter can continue to calibrate in the new scenario, preventing errors in the current scenario from adversely affecting subsequent calibrations, and ensuring calibration accuracy and efficiency.

[0033] In a branch-level electrical isolation scenario, a second set of bus-level measurement values of the bus electric energy meter is synchronously measured during a process of dynamically loading a multi-dimensional disturbance load combination on K branch circuits through a programmable load module.

[0034] In one embodiment, in the branch-level electrical isolation scenario, first, a programmable load module dynamically loads a multi-dimensional disturbance load combination on K branch circuits. These load combinations are ordered in time and are gradually loaded according to a preset time sequence. Among them, the programmable load module precisely aligns the output of each load on each branch circuit according to the time sequence, so as to simulate the response of the electricity meter under different load conditions. During the load loading process, the bus electricity meter starts to synchronously perform metering operations and real-time collects the load disturbance data on each branch circuit. After these data are processed, a second set of bus-level metering values is output, and these values reflect the actual metering situation of the electricity meter in the branch-level electrical isolation scenario. Through the time sequence control of the programmable load module, it is ensured that the loading of the load disturbance is synchronized with the metering process of the electricity meter, thereby providing accurate data support for subsequent error traceability and closed-loop calibration.

[0035] Furthermore, the present application provides a method for synchronously measuring a second set of bus-level metering values of the bus electricity meter during the process of dynamically loading a multi-dimensional disturbance load combination on K branch circuits in the branch-level electrical isolation scenario. The method includes: Collecting K historical electricity consumption data of the K branch circuits; extracting landmark load feature vectors based on the recurrence frequency of the load characteristics of the K historical electricity consumption data, and constructing K branch load feature libraries; performing orthogonal decomposition and random combination of the load feature vectors of the K branch load feature libraries, and dynamically outputting a multi-dimensional disturbance load combination, where the multi-dimensional disturbance load combination is composed of F multi-dimensional disturbance load vectors, and F≥50; stepwise loading the F multi-dimensional disturbance load vectors on the K branch circuits according to discrete time sequences through the programmable load module, and synchronously collecting the metering data of the bus electricity meter in the steady state interval to obtain the second set of bus-level metering values, where the second set of bus-level metering values includes F second bus-level metering values.

[0036] Preferably, first, historical power consumption data of each of the K branch circuits is collected. These data record the power consumption of each branch circuit over a period of time, reflecting the load fluctuations and operating states of each branch circuit. Subsequently, based on the collected historical power consumption data of the K branch circuits, the recurrence frequency of the load characteristics of each branch circuit is analyzed. This means that the frequency of repeated occurrence of the load characteristics over time is counted, and the resistive power factor value with the highest frequency is extracted as the resistive load power factor, the capacitive power factor value with the highest frequency is extracted as the capacitive load power factor, the inductive power factor value with the highest frequency is extracted as the inductive auxiliary power factor, and the non-linear load characteristics with the highest frequency are extracted as non-linear load parameters. By splicing the extracted resistive load power factor, capacitive load power factor, inductive auxiliary power factor, and non-linear load parameters, a signature load characteristic vector of the historical power consumption data of the K branch circuits is obtained, thereby constructing a load characteristic library for the K branch circuits. After that, the signature load characteristic vectors in the load characteristic libraries of the K branch loads are subjected to a similar orthogonal decomposition as described above, decomposing the complex load characteristic vectors into mutually independent parts for subsequent combination and analysis. After the orthogonal decomposition is completed, random combinations are made according to the orthogonal decomposition results to generate F different load combinations (the value of F is greater than or equal to 50), and each combination corresponds to a multi-dimensional perturbed load vector, which is used to simulate the load fluctuations that may occur under different working conditions. By adding these multi-dimensional perturbed load vectors to a set, a multi-dimensional perturbed load combination is formed. Then, according to the discrete time sequence control, the programmed load module gradually loads the F multi-dimensional perturbed load vectors in the multi-dimensional perturbed load combination into the K branch circuits. Each multi-dimensional perturbed load vector will be loaded at a specific time point to ensure that the dynamic fluctuations of the load are orderly and can simulate the load changes in actual use. During the process of loading the multi-dimensional perturbed load, the change of each load combination will cause the change of the measurement data of the watt-hour meter. Therefore, the measurement data of the bus watt-hour meter is synchronously collected within the steady-state interval of the load loading to ensure the stability and reliability of the data. Finally, the collected measurement data is summarized to obtain a second set of bus-level measurement values. This second set of bus-level measurement values includes F second bus-level measurement values, representing the performance of the bus watt-hour meter under the multi-dimensional perturbed load combination, providing the necessary data support for subsequent error calculation and calibration.

[0037] Furthermore, the present application provides an orthogonal decomposition and random combination of load characteristic vectors for the load characteristic libraries of the K branch loads, dynamically outputting a multi-dimensional perturbed load combination. The method includes: Based on the recurrence time sequence of the K branch load feature libraries in the K historical power consumption data, cross-branch load conflict identification is performed as a first load combination restriction; the bus capacity is interactively obtained as a second load combination restriction; with the first load combination restriction and the second load combination restriction as constraints, the load feature vectors of the K branch load feature libraries are orthogonally decomposed and randomly combined, and a multi-dimensional disturbance load combination is dynamically output.

[0038] Optionally, first, based on the historical electricity usage data contained in the K branch load signature libraries, the load signature recurrence time series of each branch circuit is analyzed. The recurrence time series is the recurring pattern of each load signature over time. By analyzing this time series data, potential load conflicts between different branch circuits are identified. Load conflicts refer to situations where multiple circuits may be overloaded or interfere with each other within the same time period, resulting in the inability of the loads to operate stably simultaneously. Therefore, a first load combination limit is set based on the recurring pattern to avoid these load conflicts and ensure load coordination between different circuits. After identifying load conflicts, the bus capacity is obtained through interaction. Bus capacity refers to the maximum current or power that a bus energy meter can carry. Exceeding this bus capacity will affect the normal operation of the energy meter. Therefore, a second load combination limit is set based on the bus capacity to ensure that the load combination does not exceed the bus carrying capacity. Once the first and second load combination limits are determined, the load feature vectors in the K branch load signature libraries are orthogonally decomposed. This process uses a similar approach to that described above, using principal component analysis to decompose the different load signatures into independent components for easier combination and analysis. Finally, these orthogonally decomposed eigenvectors are randomly combined to generate F different load combinations, which serve as F multidimensional perturbation load vectors. These multidimensional perturbation load vectors comply with both the first load combination constraint (avoiding cross-branch load conflicts) and the second load combination constraint (not exceeding the bus capacity). These multidimensional perturbation load combinations will be used in subsequent testing and calibration to ensure that the bus energy meter can accurately measure and calibrate under various load conditions.

[0039] Furthermore, the present application provides a signature load characteristic vector including resistive load power factor, capacitive load power factor, inductive auxiliary power factor and nonlinear load parameters.

[0040] Optionally, the signature load feature vector includes a resistive load power factor, a capacitive load power factor, an inductive auxiliary power factor, and non-linear load parameters. Among them, the resistive load power factor refers to the power factor of a pure resistive load, and the calculation method is the division of the active power of the resistive load part by the apparent power. The active power is the product of the voltage, current, and the cosine value of the phase angle between the voltage and current of the resistive load part, and the apparent power is the product of the voltage and current of the resistive load part. The capacitive load power factor reflects the phase difference caused by capacitive loads, and the calculation method is the division of the active power of the capacitive load part by the apparent power. The inductive auxiliary power factor reflects the power factor of inductive loads, usually the current lags behind the voltage, and the calculation method is the division of the active power of the inductive load and other auxiliary load parts by the apparent power. Non-linear load parameters usually cause current waveform distortion and generate harmonics. To obtain non-linear load parameters, methods such as Fourier transform are used to extract the amplitudes and frequencies of each harmonic. By calculating the harmonic content in the current and voltage waveforms, harmonic parameters of the non-linear load are obtained, including indicators such as the total harmonic distortion (THD, Total Harmonic Distortion) to reflect the characteristics of the non-linear load in this circuit.

[0041] Perform branch-level energy consumption error traceability calculation on the second set of bus-level measurement values, and perform closed-loop calibration on the bus watt-hour meter according to the calculation results.

[0042] In one embodiment, after obtaining the second set of bus-level measurement values, the theoretical energy consumption value of the multi-dimensional disturbance load combination is combined with the second set of bus-level measurement values for error traceability calculation, analyzing the deviation between the actually measured measurement value and the theoretical value, and thus outputting the second set of bus-level errors. Subsequently, analyze the fluctuations of the second set of bus-level errors, match and generate corresponding calibration strategy parameters, which are based on the characteristics of the errors and aim to compensate for measurement errors caused by load disturbances or environmental factors. Finally, according to the generated calibration strategy parameters, perform automated closed-loop calibration on the bus watt-hour meter, that is, by dynamically adjusting the calibration parameters of the bus watt-hour meter until the error is minimized, ensuring that the measurement accuracy of the bus watt-hour meter under different load conditions reaches the expected standard.

[0043] Furthermore, the present application provides a method for performing branch-level energy consumption error traceability calculation on the second set of bus-level measurement values and performing closed-loop calibration on the bus watt-hour meter according to the calculation results. The method includes: Construct a branch-level prediction array based on the theoretical energy consumption values of the multi-dimensional disturbance load combination; use the branch-level prediction array to perform error traceability calculation on the second set of bus-level measurement values, and output the second set of bus-level errors; match and output calibration strategy parameters according to the historical drift rate and the distribution fluctuation of the second set of bus-level errors; use the calibration strategy parameters to perform automatic calibration on the bus watt-hour meter.

[0044] Preferably, first, construct a branch-level prediction array according to the theoretical energy consumption values of the multi-dimensional disturbance load combination. This array reflects the ideal energy consumption situation of each branch circuit under different load combinations and provides a reference standard for subsequent error traceability calculation. Subsequently, use the branch-level prediction array to perform error traceability on the second set of bus-level measurement values, that is, compare the difference between the actually measured second set of measurement values and the theoretical energy consumption values in the prediction array to obtain the second set of bus-level errors. This second set of bus-level errors helps to identify the measurement deviation of the bus watt-hour meter under different load conditions. Then, perform distribution fluctuation analysis on the obtained second set of bus-level errors, calculate the bus-level error characteristics of the second set of bus-level errors, including bus-level range, bus-level standard deviation, bus-level skewness coefficient, and bus-level over-threshold ratio, and then determine the error distribution mode of the bus-level error characteristics to determine the current fluctuation distribution mode. Then, extract the historical drift data within a recent preset time. This historical drift data reflects the drift rate of the watt-hour meter under different operating conditions. The drift rate refers to the change speed of the measurement value of the bus watt-hour meter over time. By inputting the historical drift rate and the fluctuation distribution mode into the calibration strategy matching rule, obtain the calibration strategy that conforms to the current fluctuation, and parse out the corresponding calibration strategy parameters. These strategy parameters include the adjustment amplitude, calibration frequency, and calibration timing, etc., aiming to dynamically adjust the calibration process according to the historical drift characteristics and current error distribution of the bus watt-hour meter to ensure that the measurement accuracy of the bus watt-hour meter is corrected and optimized under future load changes. Finally, use the generated calibration strategy parameters to perform automatic closed-loop calibration on the bus watt-hour meter. This process eliminates the errors caused by load fluctuations and environmental factors by adjusting the internal calibration parameters of the bus watt-hour meter, thereby ensuring that the bus watt-hour meter can provide accurate measurement data under various working conditions.

[0045] In summary, the embodiments of the present application have at least the following technical effects: In the embodiment of the present application, after calibration is triggered, the bus electric energy meter is switched to the bus-level electrical isolation scenario through a switching relay; subsequently, in the bus-level electrical isolation scenario, after injecting a full-condition steady-state signal strategy into the bus electric energy meter, metering data extraction is performed to output a first set of bus-level metering values; then, the first set of bus-level metering values is used to compare with the full-condition theoretical values to calculate the test error, and a first set of bus-level errors is output; further, a scenario switching judgment is made according to the error distribution characteristics of the first set of bus-level errors. If it is judged to switch, the bus electric energy meter is switched to the branch-level electrical isolation scenario through the switching relay; then, in the branch-level electrical isolation scenario, during the process of dynamically loading a multi-dimensional disturbance load combination on K branch circuits through a programmable load module, the second set of bus-level metering values of the bus electric energy meter is synchronously metered; finally, a branch-level energy consumption error traceability calculation is performed on the second set of bus-level metering values, and a closed-loop calibration is performed on the bus electric energy meter according to the calculation result. These technical effects jointly solve the technical problems that in the traditional calibration process of electric energy meters, it is impossible to respond to the multi-dimensional disturbance load environment in real time, resulting in low calibration accuracy and the error distribution in complex scenarios cannot be accurately reflected, and achieve the technical effects of improving the accurate calibration ability of electric energy meters in multiple scenarios and reducing the equipment calibration time through staged electrical isolation and dynamic multi-dimensional disturbance load loading.

[0046] Embodiment 2, based on the same inventive concept as the intelligent calibration method for an electric energy meter in the foregoing embodiment, as Figure 2 shown, the present application provides an intelligent calibration system for an electric energy meter, and the system includes: A calibration trigger unit 11: After calibration is triggered, the bus electric energy meter is switched to the bus-level electrical isolation scenario through a switching relay; a first metering data extraction unit 12: In the bus-level electrical isolation scenario, after injecting a full-condition steady-state signal strategy into the bus electric energy meter, metering data extraction is performed to output a first set of bus-level metering values; a test error calculation unit 13: The first set of bus-level metering values is used to compare with the full-condition theoretical values to calculate the test error, and a first set of bus-level errors is output; a scenario switching judgment unit 14: A scenario switching judgment is made according to the error distribution characteristics of the first set of bus-level errors. If it is judged to switch, the bus electric energy meter is switched to the branch-level electrical isolation scenario through the switching relay; a second metering data extraction unit 15: In the branch-level electrical isolation scenario, during the process of dynamically loading a multi-dimensional disturbance load combination on K branch circuits through a programmable load module, the second set of bus-level metering values of the bus electric energy meter is synchronously metered; a closed-loop calibration unit 16: A branch-level energy consumption error traceability calculation is performed on the second set of bus-level metering values, and a closed-loop calibration is performed on the bus electric energy meter according to the calculation result.

[0047] Further, the calibration trigger unit 11 is further used to execute the following method: The switching relay performs a branch load locking operation to electrically disconnect the connection between the bus watt-hour meter and the K branch circuits; after the switching relay performs a grid input cut-off operation, the bus watt-hour meter is connected to a standard source, where the standard source is used to inject the full-condition steady-state signal strategy into the bus watt-hour meter.

[0048] Further, the calibration trigger unit 11 is further configured to execute the following method: Locally call the multi-source historical state data of the bus watt-hour meter; after extracting the multi-scale steady-state operation duration from the multi-source historical state data, compare and extract the minimum value to generate a timing trigger threshold; calculate the standard deviation of the active power of the multi-source historical state data through a sliding window to generate a first event trigger threshold; calculate the range ratio of the effective value of the branch circuit current based on the multi-source historical state data to generate a second event trigger threshold; input the timing trigger threshold, the first event trigger threshold, and the second event trigger threshold into a state machine to construct a calibration trigger condition matching engine.

[0049] Further, the first measurement data extraction unit 12 is further configured to execute the following method: Match the full-condition strategy information according to the application scenario of the bus watt-hour meter, where the full-condition strategy information is composed of a rated voltage condition sequence, a current condition range grading, and a rated power factor sequence; perform random perturbation on the full-condition strategy information by interacting with the multi-condition fluctuation scale, and then construct multiple groups of steady-state test signals based on orthogonal decomposition; encapsulate the multiple groups of steady-state test signals in ascending order of voltage gradient to form a full-condition steady-state signal injection sequence as the full-condition steady-state signal strategy; during the process of controlling the standard source to inject the full-condition steady-state signal injection sequence into the bus watt-hour meter in discrete time steps, synchronously execute measurement data extraction and output the first set of bus-level measurement values.

[0050] Further, the scenario switching judgment unit 14 is further configured to execute the following method: Perform multi-dimensional feature extraction on the first set of bus-level errors based on a preset correlation index to output bus-level error features, where the bus-level error features include bus-level range, bus-level standard deviation, bus-level skewness coefficient, and bus-level over-threshold ratio; determine the error distribution mode based on the bus-level error features to output the distribution mode category; match the switching decision rule library according to the distribution mode category. If the decision output is a branch-level isolation instruction, drive the switching relay to switch the bus watt-hour meter to the branch-level electrical isolation scenario.

[0051] Further, the second measurement data extraction unit 15 is further configured to execute the following method: Collect the K historical power consumption data of the K branch circuits; extract the landmark load feature vectors based on the recurrence frequency of the load characteristics of the K historical power consumption data, and construct K branch load feature libraries; perform orthogonal decomposition and random combination of the load feature vectors of the K branch load feature libraries, and dynamically output a multi-dimensional perturbed load combination, where the multi-dimensional perturbed load combination is composed of F multi-dimensional perturbed load vectors, F≥50; load the F multi-dimensional perturbed load vectors step by step in the K branch circuits according to discrete time sequences through the programmable load module, and synchronously collect the measurement data of the bus watt-hour meter in the steady state interval to obtain the second set of bus-level measurement values, where the second set of bus-level measurement values includes F second bus-level measurement values.

[0052] Further, the second measurement data extraction unit 15 is further configured to execute the following method: Based on the recurrence time sequence of the K historical power consumption data in the K branch load feature libraries, perform cross-branch load conflict identification as the first load combination limit; interactively obtain the bus capacity as the second load combination limit; use the first load combination limit and the second load combination limit as constraints, perform orthogonal decomposition and random combination of the load feature vectors of the K branch load feature libraries, and dynamically output a multi-dimensional perturbed load combination.

[0053] Further, the second measurement data extraction unit 15 is further configured to execute the following method: The landmark load feature vectors include resistive load power factor, capacitive load power factor, inductive auxiliary power factor, and non-linear load parameters.

[0054] Further, the closed-loop calibration unit 16 is further configured to execute the following method: Construct a branch-level prediction array based on the theoretical energy consumption value of the multi-dimensional perturbed load combination; use the branch-level prediction array to perform error traceability calculation on the second set of bus-level measurement values, and output the second set of bus-level errors; match and output calibration strategy parameters according to the historical drift rate and the distribution fluctuation of the second set of bus-level errors; use the calibration strategy parameters to perform automatic calibration on the bus watt-hour meter.

[0055] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of the present specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0056] The foregoing are only the preferred embodiments of the present application, and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0057] This specification and the drawings are merely exemplary descriptions of the present application, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications therein.

Claims

1. An intelligent calibration method for an electric energy meter, characterized in that, The method includes: After calibration triggering, switch the bus watt-hour meter to the bus-level electrical isolation scenario through a switching relay; In the bus-level electrical isolation scenario, after injecting the full-condition steady-state signal strategy into the bus watt-hour meter, perform metering data extraction and output the first set of bus-level metering values; Use the first set of bus-level metering values to calculate the test error by comparing with the full-condition theoretical values and output the first set of bus-level errors; Based on the error distribution characteristics of the first set of bus-level errors, judge whether to switch scenarios. If it is judged to switch, switch the bus watt-hour meter to the branch-level electrical isolation scenario through the switching relay; In the branch-level electrical isolation scenario, during the dynamic loading of the multi-dimensional disturbance load combination in K branch circuits by the programmable load module, synchronously meter the second set of bus-level metering values of the bus watt-hour meter; Perform branch-level energy consumption error traceability calculation on the second set of bus-level metering values and perform closed-loop calibration on the bus watt-hour meter according to the calculation results.

2. The intelligent calibration method for an electric energy meter according to claim 1, wherein, Perform branch-level energy consumption error traceability calculation on the second set of bus-level metering values and perform closed-loop calibration on the bus watt-hour meter according to the calculation results. The method includes: Construct a branch-level prediction array based on the theoretical energy consumption values of the multi-dimensional disturbance load combination; Use the branch-level prediction array to perform error traceability calculation on the second set of bus-level metering values and output the second set of bus-level errors; Match and output calibration strategy parameters according to the historical drift rate and the distribution fluctuation of the second set of bus-level errors; Perform automatic calibration on the bus watt-hour meter using the calibration strategy parameters.

3. The intelligent calibration method for an electric energy meter according to claim 1, wherein, After calibration triggering, switch the bus watt-hour meter to the bus-level electrical isolation scenario through a switching relay. The method includes: The switching relay performs branch load locking operations to electrically disconnect the connection between the bus watt-hour meter and K branch circuits; After the switching relay performs the power grid input cut-off operation, connect the bus watt-hour meter to the standard source, where the standard source is used to inject the full-condition steady-state signal strategy into the bus watt-hour meter.

4. The intelligent calibration method for an electricity meter according to claim 3, wherein, In the bus-level electrical isolation scenario, after injecting the full-condition steady-state signal strategy into the bus watt-hour meter, perform metering data extraction and output the first set of bus-level metering values. The method includes: Match the full-condition strategy information according to the application scenario of the bus watt-hour meter, where the full-condition strategy information consists of the rated voltage condition sequence, the current condition range division, and the rated power factor sequence; After randomly disturbing the full-condition strategy information by interacting with the multi-condition fluctuation scale, construct multiple groups of steady-state test signals based on orthogonal decomposition; Structurally encapsulate the multiple groups of steady-state test signals in ascending order of voltage gradient to form a full-condition steady-state signal injection sequence as the full-condition steady-state signal strategy; During the process of stepwise controlling the standard source to inject the full-condition steady-state signal injection sequence into the bus watt-hour meter in discrete time series, synchronously perform metering data extraction and output the first set of bus-level metering values.

5. The intelligent calibration method for an electric energy meter according to claim 1, characterized in that, In a branch-level electrical isolation scenario, during a process of dynamically loading a multi-dimensional disturbance load combination on K branch circuits through a programmable load module, a second set of bus-level measurement values of the bus electric energy meter is synchronously measured, the method comprising: Collecting K historical electricity consumption data of the K branch circuits; Based on the recurrence frequency of the load characteristics of the K historical electricity consumption data, a signature load characteristic vector is extracted to construct K branch load characteristic libraries; Performing orthogonal decomposition and random combination of load feature vectors on the K branch load feature libraries, and dynamically outputting a multidimensional disturbance load combination, wherein the multidimensional disturbance load combination is composed of F multidimensional disturbance load vectors, where F ≥ 50; The F multidimensional disturbance load vectors are loaded step by step on the K branch circuits according to a discrete time sequence through the programmable load module, and the metering data of the bus electric energy meter is synchronously collected in the steady-state interval to obtain the second group of bus-level metering values, wherein the second group of bus-level metering values includes F second bus-level metering values.

6. The intelligent calibration method for an electric energy meter according to claim 5, characterized in that, Performing orthogonal decomposition and random combination of load feature vectors on the K branch load feature libraries to dynamically output a multi-dimensional disturbance load combination, the method comprising: Performing cross-branch load conflict identification based on the recurrence time sequence of the K branch load feature libraries in the K historical power consumption data as a first load combination restriction; Interactively obtain bus capacity as the second load combination limit; Taking the first load combination restriction and the second load combination restriction as constraints, orthogonal decomposition and random combination of load feature vectors are performed on the K branch load feature libraries to dynamically output a multi-dimensional disturbance load combination.

7. The intelligent calibration method for an electricity meter according to claim 1, characterized in that, A scenario switching judgment is performed based on the error distribution characteristics of the first group of bus-level errors. If a scenario switching is determined, the bus electric energy meter is switched to a branch-level electrical isolation scenario via the switching relay. The method includes: Performing multidimensional feature extraction on the first group of bus-level errors based on preset correlation indicators, and outputting bus-level error features, wherein the bus-level error features include bus-level range, bus-level standard deviation, bus-level skewness coefficient, and bus-level exceeding threshold ratio; Determine the error distribution mode according to the bus-level error characteristics and output the distribution mode category; According to the distribution pattern category matching switching decision rule base, if the decision output is a branch-level isolation instruction, the switching relay is driven to switch the bus power meter to the branch-level electrical isolation scenario.

8. The intelligent calibration method for an electric energy meter according to claim 6, characterized in that, The characteristic load vectors include resistive load power factor, capacitive load power factor, inductive auxiliary power factor and nonlinear load parameters.

9. The intelligent calibration method for an electric energy meter according to claim 1, characterized in that, After the calibration is triggered, the bus energy meter is switched to the bus-level electrical isolation scenario by switching the relay. Previously, the method includes: Locally calling multi-source historical status data of the bus electric energy meter; After extracting the multi-scale steady-state operation duration from the multi-source historical state data, comparing and extracting the minimum value to generate a timing trigger threshold; Calculating the active power standard deviation of the multi-source historical status data through a sliding window to generate a first event trigger threshold; Calculating the range ratio of the effective value of the branch circuit current based on the multi-source historical status data to generate a second event trigger threshold; Input the timing trigger threshold, the first event trigger threshold, and the second event trigger threshold into the state machine to construct a calibration trigger condition matching engine.

10. An intelligent calibration system for an electric energy meter, characterized in that, The system is used to execute the intelligent calibration method for the electric energy meter according to any one of claims 1-9, and includes: Calibration trigger unit: After calibration is triggered, switch the bus electric energy meter to the bus-level electrical isolation scenario through a switching relay; First measurement data extraction unit: In the bus-level electrical isolation scenario, after injecting a full-condition steady-state signal strategy into the bus electric energy meter, perform measurement data extraction and output the first set of bus-level measurement values; Test error calculation unit: Calculate the test error by comparing the first set of bus-level measurement values with the full-condition theoretical values, and output the first set of bus-level errors; Scenario switching judgment unit: Make a scenario switching judgment according to the error distribution characteristics of the first set of bus-level errors. If it is judged to switch, switch the bus electric energy meter to the branch-level electrical isolation scenario through the switching relay; Second measurement data extraction unit: In the branch-level electrical isolation scenario, synchronously measure the second set of bus-level measurement values of the bus electric energy meter during the dynamic loading of the multi-dimensional disturbance load combination in the K branch circuits by the programmable load module; Closed-loop calibration unit: Perform branch-level energy consumption error traceability calculation on the second set of bus-level measurement values, and perform closed-loop calibration on the bus electric energy meter according to the calculation results.

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