Method and system for evaluating comprehensive performance of electric energy meter

Abstract

The invention provides an electric energy meter comprehensive performance evaluation method and system, and relates to the technical field of electric energy meters, and the method comprises the steps: carrying out the application load analysis through obtaining the basic specification information of a to-be-evaluated electric energy meter, and generating a non-standard load set; carrying out introduction analysis and fusion on a harmonic component, a peak current, a nonlinear power factor and a dynamic load for a non-standard load set to generate a plurality of groups of fused nonlinear introduction variables; determining a test circuit for testing the to-be-evaluated electric energy meter; inputting the multiple groups of fused nonlinear introduced variables into a dynamic load simulator to carry out load simulation on the test circuit, and recording multiple groups of precision test results of the electric energy meter; and performing comprehensive performance evaluation according to the multiple groups of electric energy meter precision test results to generate a comprehensive evaluation result. According to the invention, the technical problem of insufficient accuracy of the evaluation result caused by lack of performance evaluation means for the electric energy meter under the dynamic change load in the electric energy meter test in the prior art is solved, and the accuracy and practicability of the performance evaluation of the electric energy meter are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy meters, and in particular to a method and system for evaluating the comprehensive performance of electric energy meters. Background Art

[0002] The energy meter is a key device used to measure energy consumption in the power system and is widely used in power measurement in households, industries and commercial areas. With the continuous growth of power demand and the increasingly complex power consumption environment, the performance requirements of energy meters are becoming more stringent.

[0003] At present, the performance evaluation method of electric energy meters is usually based on standard load testing, which mainly evaluates the measurement accuracy of electric energy meters by simulating current and voltage waveforms under conventional loads (such as resistive loads). This method can effectively verify the basic functions of electric energy meters under standard working conditions. However, the actual power environment is far more complex than the standard load. Factors such as nonlinear loads, dynamic load fluctuations, and harmonic components have a significant impact on the accuracy and stability of electric energy meters. Existing evaluation methods often ignore these complex load conditions, resulting in the inability to fully verify the performance of electric energy meters in actual applications. Summary of the invention

[0004] The present application provides a method and system for comprehensive performance evaluation of an electric energy meter, which solves the technical problem in the prior art that the electric energy meter test lacks means for performance evaluation of the electric energy meter under special loads or nonlinear loads, resulting in insufficient accuracy of the test evaluation results, thereby achieving the technical effect of improving the accuracy and practicality of the electric energy meter performance evaluation.

[0005] In view of the above problems, on the one hand, the present application provides a method for comprehensive performance evaluation of an electric energy meter, the method comprising: obtaining basic specification information of the electric energy meter to be evaluated, performing application load analysis based on the basic specification information, and generating a non-standard load set; performing introduction analysis and fusion of harmonic components, peak current, nonlinear power factor and dynamic load for the non-standard load set, and generating multiple sets of fused nonlinear introduction variables; determining a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator; inputting the multiple sets of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, and recording multiple sets of electric energy meter accuracy test results; performing a comprehensive performance evaluation based on the multiple sets of electric energy meter accuracy test results, and generating a comprehensive evaluation result.

[0006] On the other hand, the present application also provides a comprehensive performance evaluation system for an electric energy meter, the system comprising: an application load analysis module, used to obtain basic specification information of the electric energy meter to be evaluated, perform application load analysis based on the basic specification information, and generate a non-standard load set; a load analysis fusion module, used to perform introduction analysis and fusion of harmonic components, peak current, nonlinear power factor and dynamic load for the non-standard load set, and generate multiple sets of fused nonlinear introduction variables; a test circuit determination module, used to determine a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator; a load simulation test module, used to input the multiple sets of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, and record multiple sets of electric energy meter accuracy test results; a comprehensive evaluation module, used to perform comprehensive performance evaluation based on the multiple sets of electric energy meter accuracy test results, and generate a comprehensive evaluation result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: Obtain the basic specification information of the electric energy meter to be evaluated, perform application load analysis based on the basic specification information, generate a non-standard load set, locate the special load conditions that the electric energy meter may face, and provide basic load data for subsequent test and evaluation. Perform introduction analysis and fusion of harmonic components, peak current, nonlinear power factor and dynamic load for the non-standard load set, generate multiple sets of fused nonlinear introduction variables, so as to more comprehensively simulate the complex load conditions of the electric energy meter in actual use, thereby improving the comprehensiveness of the evaluation. Determine a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator, providing a physical platform for subsequent load simulation. Input the multiple sets of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, simulate the working state of the electric energy meter under actual complex loads more flexibly and accurately, obtain multiple sets of electric energy meter accuracy test results, and provide a direct basis for evaluating the performance of the electric energy meter. Perform a comprehensive performance evaluation based on the multiple sets of electric energy meter accuracy test results, generate a comprehensive evaluation result, and accurately judge the performance of the electric energy meter in complex actual application scenarios.

[0008] In summary, this application introduces multiple groups of fused nonlinear introduction variables and combines them with a dynamic load simulator to comprehensively consider the complex load conditions that the electric energy meter may encounter in actual applications, including the combined influence of harmonic components, peak currents, nonlinear power factors, and dynamic loads, thereby obtaining test results that accurately reflect the performance of the electric energy meter. The final generated comprehensive evaluation results can more comprehensively and accurately evaluate the comprehensive performance of the electric energy meter in actual working scenarios, thereby improving the accuracy and practicality of the electric energy meter performance evaluation.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a method for evaluating the comprehensive performance of an electric energy meter provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of a flow chart for generating multiple groups of fused nonlinear introduced variables in a comprehensive performance evaluation method for an electric energy meter provided in an embodiment of the present application.

[0012] Figure 3 A schematic diagram of the structure of an electric energy meter comprehensive performance evaluation system provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: application load analysis module 10 , load analysis fusion module 20 , test circuit determination module 30 , load simulation test module 40 , comprehensive evaluation module 50 . DETAILED DESCRIPTION

[0014] The embodiment of the present application provides a comprehensive performance evaluation method and system for an electric energy meter, obtains basic specification information of the electric energy meter, analyzes and generates a non-standard load set, further analyzes and fuses the introduction of harmonic components, peak currents, nonlinear power factors, and dynamic loads for the non-standard load set, generates multiple sets of fused nonlinear introduction variables, and then uses a test circuit connected to a dynamic load simulator to perform load simulation and record accuracy test results, and finally performs a comprehensive performance evaluation based on these results. The embodiment of the present application solves the technical problem that the electric energy meter test in the prior art lacks a means of evaluating the performance of the electric energy meter under special loads or nonlinear loads, resulting in insufficient accuracy of the test evaluation results, and achieves the technical effect of improving the accuracy and practicality of the performance evaluation of the electric energy meter.

[0015] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a method for evaluating the comprehensive performance of an electric energy meter, the method comprising: Step S1: obtaining basic specification information of the electric energy meter to be evaluated, performing application load analysis based on the basic specification information, and generating a non-standard load set.

[0016] Specifically, the energy meter to be evaluated can be any energy meter that needs to be tested and evaluated for performance. The non-standard load set refers to a set of special load types that the energy meter may encounter outside the standard load conditions. Non-standard loads include some uncommon load modes, such as irregular waveform loads, high harmonic loads, or sudden loads.

[0017] By querying the product manual and product identification of the energy meter or from the database provided by the manufacturer of the energy meter, obtain the basic specification information of the energy meter to be evaluated, including voltage, current, power factor, measurement accuracy, frequency response, etc. These specification information can be used to determine the working range and capability of the energy meter under different conditions. Based on these basic specification information, perform application load analysis to determine the load conditions that the energy meter to be evaluated may be connected to. For example, in a household electricity scenario, the energy meter may be connected to various electrical appliances, such as refrigerators, televisions, air conditioners, etc. The power and working characteristics of these appliances constitute the load conditions of the energy meter. From the load conditions that the energy meter may be connected to, select and determine the loads that do not have standard characteristics, and generate a non-standard load set, such as the large current impact when the air conditioner compressor is started, and the irregular current changes when the refrigerator compressor is periodically started.

[0018] Step S2: performing introduction analysis and fusion of harmonic components, peak current, nonlinear power factor and dynamic load for the non-standard load set to generate multiple groups of fused nonlinear introduction variables.

[0019] Specifically, the harmonic components, peak current, nonlinear power factor and dynamic load are analyzed for each non-standard load in the non-standard load set, and the results of the harmonic components, peak current, nonlinear power factor and dynamic load obtained by the analysis are fused to generate multiple sets of fused nonlinear introduction variables. By performing multi-dimensional introduction analysis and fusion on the previously extracted non-standard load set, multiple fused nonlinear introduction variables are generated, thereby more comprehensively reflecting the characteristics of non-standard loads and providing an accurate data source for subsequent load simulations, so as to accurately simulate the working state of the energy meter in real applications and improve the reliability and accuracy of test evaluation.

[0020] Step S3: determining a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator.

[0021] Specifically, the test circuit is a circuit specially constructed for testing the energy meter to be evaluated, which contains various necessary circuit components and connection methods to meet the test requirements. A dynamic load simulator is a device that can simulate the characteristics of different types of loads, especially suitable for simulating dynamic fluctuations and rapid changes of loads. A dynamic load simulator can generate electrical characteristics similar to the actual load according to the set parameters, such as the size of the load, the frequency of change, the harmonic content, etc., to simulate changes such as motor starting, load mutation, equipment switching operation, etc.

[0022] Determine the structure of the test circuit according to the type and specifications of the energy meter to be evaluated and the items to be tested. If it is a single-phase energy meter, the test circuit may be a simple single-phase circuit structure; if it is a three-phase energy meter, a three-phase circuit structure needs to be constructed. When determining the circuit structure, the basic specification information such as the rated voltage and rated current of the test energy meter should be taken into account. For example, for a single-phase energy meter with a rated voltage of 220V and a rated current of 5A, the power supply voltage of the test circuit should be set to about 220V, and circuit elements with appropriate rated currents should be selected, such as 5A fuses. Then, select appropriate circuit elements to construct the test circuit according to the selected circuit structure and the type and specifications of the energy meter to be evaluated. Circuit elements include voltmeters, ammeters, power supplies, and various protection elements.

[0023] Select the appropriate connection cable according to the interface type of the test circuit and the dynamic load simulator, and connect the constructed test circuit to the dynamic load simulator. By connecting the dynamic load simulator, the test circuit can generate different types of load waveforms, including current waveforms with harmonic components, instantaneous current spikes, etc. Under the control of the simulator, the test circuit can quickly switch between different load states, allowing the energy meter to perform accuracy tests under these dynamic conditions, and thus comprehensively evaluate the performance of the energy meter under dynamic and complex load environments.

[0024] Step S4: inputting the multiple groups of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, and recording multiple groups of electric energy meter accuracy test results.

[0025] Specifically, multiple sets of fused nonlinear introduction variables are input into the dynamic load simulator. The dynamic load simulator will adjust its output characteristics according to these input variables, and generate corresponding load characteristics such as voltage and current changes in the test circuit to simulate the load conditions represented by these variables. After the dynamic load simulator generates the simulated load, the test circuit will perform accuracy tests on these load conditions through the electric energy meter. During the test, the measurement data of the electric energy meter under different load simulation conditions are recorded, the actual measurement results of the electric energy meter are compared with the simulated load values, and the measurement error of the electric energy meter is calculated, thereby obtaining multiple sets of electric energy meter accuracy test results. For example, when the electric energy meter is applied with a load with harmonic components and spike current, the reading of the electric energy meter is recorded and compared with the theoretical value. The accuracy test results of the electric energy meter may show distortion of the current waveform, error of the spike current, or measurement error under power factor change.

[0026] By introducing multiple sets of fused nonlinear variables into the dynamic load simulator, simulating the impact of different load conditions on the accuracy of the electric energy meter, and recording the test results of the electric energy meter under these conditions, the performance of the electric energy meter under complex load environments can be comprehensively evaluated, and then its accuracy, stability and reliability in practical applications can be determined.

[0027] Step S5: Perform comprehensive performance evaluation based on the multiple groups of electric energy meter accuracy test results to generate a comprehensive evaluation result.

[0028] Specifically, the data of multiple groups of electric energy meter accuracy test results are collated and analyzed, and the accuracy performance of the electric energy meter under various different working conditions (i.e., different load simulation conditions) is comprehensively considered to judge the overall performance of the electric energy meter, thereby obtaining a comprehensive evaluation result. This result can intuitively reflect the comprehensive performance of the electric energy meter under the influence of different load characteristics, such as whether the electric energy meter meets the prescribed accuracy standards, reliability under complex load conditions, etc. For example, the average error, maximum error, error distribution range, etc. can be selected as evaluation indicators, and the accuracy test results of multiple groups of electric energy meters can be quantified according to the evaluation indicators to obtain a comprehensive evaluation result.

[0029] Further, such as Figure 2 As shown, step S2 of the embodiment of the present application also includes: Step S21: extracting a plurality of non-standard loads from the non-standard load set.

[0030] Step S22: performing separate introduction analysis of harmonic components, peak currents, nonlinear power factors and dynamic loads on the multiple non-standard loads, and establishing multiple groups of independent nonlinear introduction variables.

[0031] Step S23: performing connection complexity analysis on the multiple non-standard loads to establish a connection complexity space.

[0032] Step S24: Taking the connection complexity space as a constraint, the multiple groups of independent nonlinear introduction variables are fused under different connection complexities to generate the multiple groups of fused nonlinear introduction variables, wherein any group of fused nonlinear introduction variables includes fused harmonic components, peak current, nonlinear power factor and dynamic load.

[0033] Specifically, multiple representative non-standard loads are extracted one by one from the non-standard load set. These loads usually have different electrical characteristics, for example, some loads may have higher harmonic components, and others may cause peak current or nonlinear power factor. By extracting these loads, a group of representative non-standard loads can be obtained, which provides basic data for subsequent analysis.

[0034] Each non-standard load is analyzed separately for introduction, including harmonic components, peak current, nonlinear power factor and dynamic load. For the analysis of harmonic components, a spectrum analyzer can be used. Connect the spectrum analyzer to the circuit of the non-standard load, measure the harmonic components in its current or voltage waveform, and determine the size and frequency of the harmonic components. For the analysis of peak current, use current sensors and oscilloscopes. The current sensor measures the current in the load circuit, and the oscilloscope captures the instantaneous change of the current to determine the size of the peak current, the time of occurrence and the duration. For the analysis of non-linear power factor, a power analyzer is used. The power analyzer measures the input voltage, current and power of the non-standard load, and obtains the non-linear power factor through calculation. For the analysis of dynamic load, the voltage, current, power and other parameters of the non-standard load under different working conditions are collected, and the change rules of these parameters over time are analyzed to determine the characteristics of the dynamic load. Finally, these analysis results of each non-standard load are sorted out to establish multiple sets of independent nonlinear introduction variables.

[0035] Observe the connection relationship between multiple non-standard loads, such as whether the loads are connected in series, parallel, or in a more complex mixed connection mode, as well as the number of connection lines, the number of connection points, and other factors. According to the characteristics of these connection relationships, establish a connection complexity space. This connection complexity space is an abstract concept space that describes the connection complexity of multiple non-standard loads. In this space, the connection complexity between loads can be quantified through some indicators (such as the topological structure of the connection, the number of connections, etc.).

[0036] Under the constraints of the connection complexity space, the influence of different connection complexities on independent nonlinear introduced variables is considered, and multiple groups of independent nonlinear introduced variables are fused. For example, when the connection complexity is high, the loads may affect each other, resulting in changes in factors such as harmonic components and peak currents. For each group of independent nonlinear introduced variables, the harmonic components, peak currents, nonlinear power factors, and dynamic loads are fused according to different situations in the connection complexity space (such as different connection topologies or number of connections) to generate multiple groups of fused nonlinear introduced variables, each of which contains information such as fused harmonic components, peak currents, nonlinear power factors, and dynamic loads.

[0037] The above steps extract load characteristics from non-standard loads, analyze the impact of each characteristic on the electric energy meter separately, and then understand the interaction between loads through connection complexity analysis. Finally, these factors are integrated to generate more accurate nonlinear introduction variables, providing more comprehensive and accurate data support for the performance evaluation of the electric energy meter under complex load environments.

[0038] Furthermore, step S22 further includes: Step S221: collecting historical introduction features of harmonic components, peak currents, nonlinear power factors and dynamic loads for the multiple non-standard loads to generate multiple historical introduction variable sets.

[0039] Step S222: Draw probability density curves for the multiple historically introduced variable sets, locate density concentration areas for mean value calculation, and generate the multiple groups of independent nonlinear introduced variables.

[0040] Specifically, the relevant operating data of multiple non-standard loads are obtained through a data recording device or a historical data storage system of a monitoring system. These historical operating data contain records of various operating parameters such as voltage, current, power, etc. of non-standard loads at different time points. The data of each non-standard load obtained is analyzed to extract the historical introduction characteristics of harmonic components, peak currents, nonlinear power factors, and dynamic loads. For example, the harmonic component characteristics are extracted by Fourier transforming the historical voltage and current data; the peak current characteristics are determined by finding the peak value in the current data; the nonlinear power factor characteristics are obtained by calculating the relationship between power and voltage and current; and the dynamic load characteristics are determined by analyzing the load changes at different times. These historical introduction characteristics of each non-standard load are sorted out separately to generate multiple historical introduction variable sets.

[0041] For each set of historically introduced variables, statistical analysis software is used to draw a probability density curve to intuitively display the distribution probability of each historically introduced variable in different value ranges. By observing the peak of the curve and the data distribution near the peak, the density concentration area is located on the drawn probability density curve, that is, the frequent occurrence area of ​​each historically introduced variable.

[0042] For each non-standard load's historically introduced variables, such as harmonic components, peak current, nonlinear power factor, and dynamic load, their mean values ​​in the density concentration area are calculated respectively, and these mean values ​​are used as components of the independent nonlinear introduced variables of the non-standard load, ultimately generating multiple groups of independent nonlinear introduced variables corresponding to multiple non-standard loads.

[0043] Furthermore, step S23 further includes: Step S231: taking the multiple non-standard loads as constraints, collecting a historical load connection data set.

[0044] Step S232: performing complexity evaluation on the historical load connection data set to generate multiple complexity indices, wherein the dimensions of complexity evaluation include the number and density of types of non-standard loads, and the complexity index is proportional to the number and density of types.

[0045] Step S233: clustering the multiple complexity indicators according to a preset complexity deviation, selecting a historical load connection data corresponding to a complexity indicator in each clustering result to construct a topological connection network between loads, and generating multiple load connection networks.

[0046] Step S234: Establishing the connection complexity space using the multiple load connection networks.

[0047] Specifically, the complexity index is a quantitative value generated according to the complexity evaluation result, which reflects the complexity of the load connection. The larger the complexity index is, the higher the complexity of the load connection is.

[0048] Collect historical load connection data sets related to these non-standard loads from a historical database or data recording system. This data set contains the connection information between each non-standard load, such as the connection method between the loads, the time characteristics of the connection, the type of connected loads, etc., which can be derived from the historical records of previous monitoring, maintenance or design of similar load systems.

[0049] For the collected historical load connection data set, the complexity evaluation is performed from the two dimensions of the number of types and density of non-standard loads. For example, the number of different types of non-standard loads in the data set can be counted to calculate the number of types; the connection density is determined based on the distribution of the loads and the tightness of the connection. The number of types and density corresponding to each non-standard load are weighted and summed to generate the complexity index of the non-standard load. The complexity index is proportional to the number of types and density, that is, the more types and the greater the density, the higher the complexity index.

[0050] According to the specific analysis requirements and the preset complexity deviation, multiple complexity indicators are clustered. Exemplarily, the complexity indicators are sorted in ascending order according to their size to obtain a complexity indicator sequence, the smallest complexity indicator is selected in this sequence (i.e., the first in the sequence), the remaining complexity indicators are traversed, and the indicators whose deviation from the smallest complexity indicator is less than the preset complexity deviation are grouped together to obtain a cluster. All complexity indicators in the cluster are deleted from the complexity indicator sequence, and then the smallest complexity indicator is reselected in the new sequence, and the above operation is repeated to obtain multiple clustering results.

[0051] In each clustering result, select a historical load connection data corresponding to a complexity index, use the loads in the historical load connection data as nodes, and the connection relationships as edges to build a topological connection network between the loads. Traverse multiple clustering results to build a topological connection network, thereby generating multiple load connection networks. The selection of historical load connection data can be random selection or the central value in each cluster.

[0052] A connection complexity space is established based on multiple load connection networks. Each point in the complexity space represents a specific load connection method and the complexity corresponding to the method. In this space, various characteristics of the load connection network (such as the number of nodes, the number of edges, the connectivity of the network, etc.) can be analyzed and compared, thereby comprehensively describing the complexity of the load connection.

[0053] Furthermore, step S24 further includes: Step S241: for harmonic components, peak currents, nonlinear power factors and dynamic loads, fusion samples corresponding to parallel non-standard loads and series non-standard loads are collected.

[0054] Step S242: using the fusion samples to identify the series fusion relationship and parallel fusion relationship of the harmonic components, peak current, nonlinear power factor and dynamic load.

[0055] Step S243: According to the series fusion relationship and the parallel fusion relationship, the multiple groups of independent nonlinear introduction variables are fused under different connection complexities in the connection complexity space to generate the multiple groups of fused nonlinear introduction variables.

[0056] Specifically, when introducing independent nonlinear variables, each load connection method may have different effects on the measurement results of the electric energy meter, so different processing methods need to be adopted according to different load connection methods. The corresponding harmonic components, peak currents, nonlinear power factors and dynamic loads are collected in the circuits of parallel non-standard loads and series non-standard loads as parallel fusion samples and series fusion samples.

[0057] The collected series fusion samples are analyzed to identify the relationship patterns of the interaction and mutual influence of harmonic components, peak current, nonlinear power factor and dynamic load in the case of series non-standard loads, that is, the series fusion relationship. For example, in the fusion samples of series non-standard loads, by establishing a mathematical model (such as a multivariate regression model) or data mining analysis (such as association rule mining), it is observed how the harmonic components change with the changes in peak current, nonlinear power factor and dynamic load when the loads are connected in series, so as to determine the series fusion relationship between them. Similarly, the fusion samples of parallel non-standard loads are analyzed to identify the relationship patterns of the interaction and mutual influence of harmonic components, peak current, nonlinear power factor and dynamic load in the case of parallel non-standard loads, that is, the parallel fusion relationship.

[0058] According to the identified series fusion relationship and parallel fusion relationship, a set of independent nonlinear introduction variables (harmonic components, peak current, nonlinear power factor and dynamic load) corresponding to each non-standard load are fused according to different connection complexities in the connection complexity space to generate multiple sets of fused nonlinear introduction variables. Exemplarily, for different complexities in the connection complexity space, the corresponding load connection network is decomposed into basic series and parallel connection combinations. For the series area, the fusion is performed according to the identified series fusion relationship; for the parallel area, the fusion is performed according to the identified parallel fusion relationship.

[0059] In load modes with high connection complexity, the nonlinear behaviors of multiple loads may interact and affect the measurement of the energy meter. In this case, by fusing these introduced variables, a new fused nonlinear introduced variable can be obtained, which can better predict the accuracy of the energy meter under complex load environments, thereby providing more comprehensive data support for the final comprehensive performance evaluation.

[0060] Furthermore, step S1 further includes: Step S11: searching for application scenarios according to the basic specification information, and generating an electric energy meter application circuit.

[0061] Step S12: extracting non-standard loads in the energy meter application circuit to generate the non-standard load set.

[0062] Specifically, when obtaining a set of non-standard loads, a method of analyzing typical application scenarios can be used. First, identify and select possible usage scenarios or working environments of the energy meter to be evaluated based on the basic specification information of the energy meter. These application scenarios are the operating environments of the energy meter in actual applications, such as power systems of homes, industries, commerce, or special equipment. In each scenario, the load characteristics and working conditions that the energy meter may face are different. Based on the retrieved application scenarios, generate an energy meter application circuit that meets the actual application, that is, the circuit formed by the energy meter connected to other devices in the application scenario. For example, for an energy meter with a rated voltage of 220V and a rated current of 5A, the application scenario retrieval will find scenarios such as ordinary family residences, and then generate an application circuit in a household electricity scenario: the energy meter is connected to the household line, and then connected to each branch, which is connected to devices such as lights and sockets.

[0063] After determining the application circuit, extract non-standard loads in the energy meter application circuit. These non-standard loads refer to those load types that are different from pure resistive loads. Their current and voltage waveforms may no longer be sinusoidal, but contain harmonic components or rapidly changing currents. For example, a motor driven by a frequency converter will generate strong harmonics, and a switching power supply will introduce spike currents. These loads have a great impact on the measurement accuracy of the energy meter, so they need to be extracted separately. By analyzing various types of equipment in the energy meter application circuit, these loads with special electrical characteristics are extracted, and a non-standard load set is generated, which contains all special load types that may affect the accuracy of the energy meter, providing comprehensive and real test conditions for subsequent accuracy testing and comprehensive evaluation.

[0064] Furthermore, step S5 further includes: Step S51: performing fluctuation analysis on the multiple groups of electric energy meter accuracy test results to generate fluctuation indicators.

[0065] Step S52: Draw probability density curves for the multiple groups of electric energy meter accuracy test results, locate density concentration areas and calculate the mean, and generate a test mean.

[0066] Step S53: compensating the test mean with the fluctuation index to generate the comprehensive evaluation result.

[0067] Specifically, multiple groups of electric energy meter accuracy test results recorded in step S4 are obtained, and fluctuation analysis is performed on these test results through statistical analysis tools to calculate the degree of fluctuation between different electric energy meter accuracy test results to determine the stability of the accuracy of the electric energy meter under different test conditions. A value used to quantify the degree of fluctuation of the electric energy meter accuracy test results, i.e., a fluctuation index, is generated through fluctuation analysis. The fluctuation index can be a statistic such as standard deviation and variance.

[0068] For each set of electric energy meter accuracy test results, draw a probability density curve of the electric energy meter accuracy test results, with the horizontal axis representing the measured value of the electric energy meter and the vertical axis representing the probability of the measured value. Analyze the probability density curve to locate the density concentration area, that is, the area where the electric energy meter measurement accuracy values ​​are most concentrated or most likely to occur. Then calculate the mean of the accuracy test results in this area, that is, the test mean. This test mean comprehensively considers the value of the accuracy test results under different distribution densities, and can reflect the overall performance of the electric energy meter in multiple tests.

[0069] The test mean is compensated based on the fluctuation index to correct the accuracy error caused by load fluctuation, nonlinear load, etc. If the fluctuation index is large, it means that the data uncertainty is large, and the test mean needs to be adjusted to a certain extent (such as reducing or increasing the test mean by a certain proportion according to the size of the fluctuation index), and finally a comprehensive evaluation result is generated, so that it can more accurately reflect the performance of the electric energy meter under actual use conditions.

[0070] In summary, the method for evaluating the comprehensive performance of an electric energy meter provided in the embodiment of the present application has the following technical effects: Through application scenario retrieval, the application circuit of the electric energy meter is constructed, and then the non-standard load set is extracted, which provides an object for subsequent analysis of special load conditions and helps to comprehensively evaluate the performance of the electric energy meter in practical applications. A separate nonlinear factor analysis is performed on the non-standard load, and independent variables are established. At the same time, the fusion variables are constructed considering the connection complexity, and multiple sets of fusion nonlinear introduction variables are generated to more accurately simulate the working state of the electric energy meter under complex loads. The test circuit is determined and connected to the dynamic load simulator, which provides a physical platform for subsequent load simulation. The multiple sets of fusion nonlinear introduction variables are input into the dynamic load simulator to perform load simulation on the test circuit, and the working state of the electric energy meter under actual complex loads is simulated more flexibly and accurately, and multiple sets of electric energy meter accuracy test results are obtained, which provide a direct basis for evaluating the performance of the electric energy meter. The multiple sets of electric energy meter accuracy test results are used for fluctuation analysis and mean calculation, and the mean is compensated according to the fluctuation index to generate a comprehensive evaluation result, and the performance of the electric energy meter in complex practical application scenarios is accurately judged.

[0071] In general, the embodiment of the present application introduces multiple groups of fused nonlinear introduction variables and combines them with a dynamic load simulator to more comprehensively and realistically simulate the performance of the electric energy meter in the actual power environment, especially the adaptability and accuracy under complex nonlinear loads, harmonic components, peak currents and dynamic loads. Through comprehensive performance evaluation, the accuracy of the evaluation is improved, and the evaluation results can better reflect the performance of the electric energy meter in a variable power environment, providing a scientific basis for the performance optimization of the electric energy meter and improving the practicality of the performance evaluation.

[0072] Embodiment 2, as Figure 3 As shown, an embodiment of the present application provides a comprehensive performance evaluation system for an electric energy meter, the system comprising: The application load analysis module 10 is used to obtain basic specification information of the electric energy meter to be evaluated, perform application load analysis based on the basic specification information, and generate a non-standard load set.

[0073] The load analysis and fusion module 20 is used to analyze and fuse the introduction of harmonic components, peak current, nonlinear power factor and dynamic load for the non-standard load set, and generate multiple groups of fused nonlinear introduction variables.

[0074] The test circuit determination module 30 is used to determine a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator.

[0075] The load simulation test module 40 is used to input the multiple groups of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, and record multiple groups of electric energy meter accuracy test results.

[0076] The comprehensive evaluation module 50 is used to perform a comprehensive performance evaluation based on the multiple groups of electric energy meter accuracy test results to generate a comprehensive evaluation result.

[0077] Furthermore, the load analysis fusion module 20 of the embodiment of the present application is also used to perform the following steps: Extract multiple non-standard loads from the non-standard load set; perform separate introduction analysis on the harmonic components, peak currents, nonlinear power factors and dynamic loads of the multiple non-standard loads to establish multiple groups of independent nonlinear introduction variables; perform connection complexity analysis on the multiple non-standard loads to establish a connection complexity space; with the connection complexity space as a constraint, fuse the multiple groups of independent nonlinear introduction variables at different connection complexities to generate the multiple groups of fused nonlinear introduction variables, wherein any group of fused nonlinear introduction variables includes fused harmonic components, peak currents, nonlinear power factors and dynamic loads.

[0078] Furthermore, the load analysis fusion module 20 of the embodiment of the present application is also used to perform the following steps: For the multiple non-standard loads, historical introduction features of harmonic components, peak currents, nonlinear power factors and dynamic loads are collected to generate multiple sets of historical introduction variables; probability density curves are drawn for the multiple sets of historical introduction variables, and density concentration areas are located to perform mean calculation to generate the multiple groups of independent nonlinear introduction variables.

[0079] Furthermore, the load analysis fusion module 20 of the embodiment of the present application is also used to perform the following steps: Taking the multiple non-standard loads as constraints, historical load connection data sets are collected; complexity evaluation is performed on the historical load connection data sets to generate multiple complexity indices, wherein the dimensions of complexity evaluation include the number and density of types of non-standard loads, and the complexity index is proportional to the number and density of types; the multiple complexity indices are clustered according to a preset complexity deviation, and a historical load connection data corresponding to a complexity indicator is selected in each clustering result to construct a topological connection network between loads, generating multiple load connection networks; the connection complexity space is established using the multiple load connection networks.

[0080] Furthermore, the load analysis fusion module 20 of the embodiment of the present application is also used to perform the following steps: For harmonic components, peak currents, nonlinear power factors and dynamic loads, fusion samples corresponding to parallel non-standard loads and series non-standard loads are collected respectively; the series fusion relationship and the parallel fusion relationship of the harmonic components, peak currents, nonlinear power factors and dynamic loads are identified using the fusion samples; according to the series fusion relationship and the parallel fusion relationship, the multiple groups of independent nonlinear introduced variables are fused at different connection complexities in the connection complexity space to generate the multiple groups of fused nonlinear introduced variables.

[0081] Furthermore, the application load analysis module 10 of the embodiment of the present application is also used to perform the following steps: An application scenario search is performed according to the basic specification information to generate an electric energy meter application circuit; non-standard loads are extracted from the electric energy meter application circuit to generate the non-standard load set.

[0082] Furthermore, the comprehensive evaluation module 50 of the embodiment of the present application is also used to perform the following steps: Perform fluctuation analysis on the multiple groups of electricity meter accuracy test results to generate fluctuation indexes; draw probability density curves on the multiple groups of electricity meter accuracy test results, locate density concentration areas and calculate means to generate test means; compensate the test means with the fluctuation index to generate the comprehensive evaluation results.

[0083] Through the above-mentioned detailed description of a comprehensive performance evaluation method for an electric energy meter in this specification, those skilled in the art can clearly understand a comprehensive performance evaluation system for an electric energy meter in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1 and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the comprehensive performance of an electric energy meter, characterized in that: include: Obtain basic specification information of the electric energy meter to be evaluated, perform application load analysis based on the basic specification information, and generate a non-standard load set; For the non-standard load set, the introduction analysis of harmonic components, peak current, nonlinear power factor and dynamic load is performed and integrated to generate multiple groups of integrated nonlinear introduction variables; Determining a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator; Inputting the multiple groups of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, and recording multiple groups of electric energy meter accuracy test results; A comprehensive performance evaluation is performed based on the multiple groups of electric energy meter accuracy test results to generate a comprehensive evaluation result.

2. A method for evaluating comprehensive performance of an electric energy meter as claimed in claim 1, characterized in that: For the non-standard load set, the introduction analysis of harmonic components, peak current, nonlinear power factor and dynamic load is performed and integrated to generate multiple groups of integrated nonlinear introduction variables, including: Extracting multiple non-standard loads from the non-standard load set; Performing separate introduction analysis of harmonic components, peak currents, nonlinear power factors and dynamic loads on the multiple non-standard loads, and establishing multiple groups of independent nonlinear introduction variables; Performing connection complexity analysis on the multiple non-standard loads to establish a connection complexity space; Taking the connection complexity space as a constraint, the multiple groups of independent nonlinear introduction variables are fused under different connection complexities to generate the multiple groups of fused nonlinear introduction variables, wherein any group of fused nonlinear introduction variables includes fused harmonic components, peak current, nonlinear power factor and dynamic load.

3. A method for evaluating comprehensive performance of an electric energy meter as claimed in claim 2, characterized in that: Perform separate introduction analysis of harmonic components, peak current, nonlinear power factor and dynamic load on the multiple non-standard loads, and establish multiple groups of independent nonlinear introduction variables, including: For the multiple non-standard loads, historical introduction characteristics of harmonic components, peak currents, nonlinear power factors, and dynamic loads are collected to generate multiple historical introduction variable sets; Probability density curves are drawn for the multiple historically introduced variable sets, and mean values ​​are calculated in the density concentration areas to generate the multiple groups of independent nonlinear introduced variables.

4. The method for evaluating comprehensive performance of an electric energy meter according to claim 2, characterized in that: Performing connection complexity analysis on the multiple non-standard loads to establish a connection complexity space includes: Taking the plurality of non-standard loads as constraints, collecting a historical load connection data set; Performing complexity evaluation on the historical load connection data set to generate multiple complexity indicators, wherein the dimensions of complexity evaluation include the number and density of types of non-standard loads, and the complexity indicator is proportional to the number and density of types; Clustering the multiple complexity indicators according to a preset complexity deviation, selecting historical load connection data corresponding to a complexity indicator in each clustering result to construct a topological connection network between loads, and generating multiple load connection networks; The connection complexity space is established with the plurality of load connection networks.

5. A method for evaluating comprehensive performance of an electric energy meter as claimed in claim 4, characterized in that: Taking the connection complexity space as a constraint, fusing the multiple groups of independent nonlinear introduction variables under different connection complexities to generate the multiple groups of fused nonlinear introduction variables, including: For harmonic components, peak current, nonlinear power factor and dynamic load, collect fusion samples corresponding to parallel non-standard loads and series non-standard loads respectively; Using the fusion samples to identify the series fusion relationship and parallel fusion relationship of harmonic components, peak current, nonlinear power factor and dynamic load; According to the series fusion relationship and the parallel fusion relationship, the multiple groups of independent nonlinear introduction variables are fused under different connection complexities in the connection complexity space to generate the multiple groups of fused nonlinear introduction variables.

6. The method for evaluating comprehensive performance of an electric energy meter according to claim 1, characterized in that: The application load analysis is performed based on the basic specification information to generate a non-standard load set, including: Perform application scenario retrieval based on the basic specification information to generate an electric energy meter application circuit; Non-standard loads are extracted in the electric energy meter application circuit to generate the non-standard load set.

7. The method for evaluating comprehensive performance of an electric energy meter according to claim 1, characterized in that: Perform a comprehensive performance evaluation based on the multiple groups of electric energy meter accuracy test results to generate a comprehensive evaluation result, including: Performing fluctuation analysis on the multiple groups of electric energy meter accuracy test results to generate fluctuation indicators; Draw probability density curves for the multiple groups of electric energy meter accuracy test results, locate density concentration areas and calculate mean values ​​to generate test mean values; The test mean is compensated by the fluctuation index to generate the comprehensive evaluation result.

8. A comprehensive performance evaluation system for electric energy meters, characterized in that: The system is used to execute the method for evaluating the comprehensive performance of an electric energy meter according to any one of claims 1 to 7, comprising: An application load analysis module, used to obtain basic specification information of the electric energy meter to be evaluated, perform application load analysis based on the basic specification information, and generate a non-standard load set; A load analysis and fusion module is used to analyze and fuse the introduction of harmonic components, peak current, nonlinear power factor and dynamic load for the non-standard load set, and generate multiple groups of fused nonlinear introduction variables; A test circuit determination module, used to determine a test circuit for testing the electric energy meter to be evaluated, wherein the test circuit is connected to a dynamic load simulator; A load simulation test module, used for inputting the multiple groups of fused nonlinear introduction variables into the dynamic load simulator to perform load simulation on the test circuit, and recording multiple groups of electric energy meter accuracy test results; The comprehensive evaluation module is used to perform a comprehensive performance evaluation based on the multiple groups of electric energy meter accuracy test results to generate a comprehensive evaluation result.

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