Intelligent verification and detection method for large-current switch built-in electric energy meter

Abstract

The invention discloses an intelligent verification and detection method for a large-current switch built-in electric energy meter, and relates to the technical field of electric energy meter detection and calibration. Outputting the difference between the output data of the standard electric energy meter and the output data of the tested electric energy meter through the real-time error model; function detection is carried out; and dynamic self-calibration is carried out based on real-time analysis detection data. According to the intelligent verification and detection method for the large-current switch built-in type electric energy meter provided by the invention, the real-time error model is constructed, so that the real-time performance and accuracy of detection are remarkably improved. Through absolute error and relative error models, the contribution of each parameter to the error can be accurately quantified, and guidance is provided for design optimization of the electric energy meter. The starting, creeping, cost control functions and communication functions are detected through the function detection module, the multifunctionality and the performance stability of the electric energy meter are guaranteed, and better effects are achieved in the aspects of the detection precision and adaptability and the adaptability of the system to complex working conditions.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy meter detection and calibration, and in particular to an intelligent calibration and detection method for an electric energy meter with a built-in large current switch. Background Art

[0002] With the rapid development of smart grids and modern power systems, the detection and calibration technology of electric energy meters has gradually evolved towards high precision, multi-function and intelligence. The detection of traditional high-current switch electric energy meters relies on manual operation or a single electrical parameter testing device, which shows certain limitations when dealing with complex working conditions (such as harmonic interference and multiple load conditions). In recent years, with the introduction of digital signal processing, artificial intelligence and Internet of Things technologies, automated and intelligent electric energy meter calibration equipment has gradually become an industry trend. This type of equipment can quickly and efficiently complete the detection and calibration of electric energy meters by integrating digital signal synthesis, high-precision error calculation and remote control modules, providing strong technical support for the operation and management of power systems.

[0003] Although the existing technology has made certain progress in the field of electric energy meter detection, the following problems still exist: Insufficient detection accuracy: Traditional detection equipment is difficult to achieve high-precision error analysis under complex signal conditions (such as high-order harmonic interference, low power factor load), especially for the detection of starting and creeping performance in low current or low power state. Functional limitations: Most existing equipment focuses on single parameter detection and lacks comprehensive testing support for comprehensive performance such as electric energy meter communication capabilities and fee control functions. Weak real-time calibration capability: Most devices can only perform static detection and lack dynamic self-calibration functions. They cannot adjust the input signal in real time according to the test data, resulting in the accuracy of the detection results being restricted by the environment and equipment characteristics. Insufficient harmonic distortion processing: The impact of harmonics on the measurement results of electric energy meters has not been effectively quantified. The deficiencies of existing technologies in harmonic distortion analysis and calibration make it difficult to meet the needs of complex working conditions in modern power grids. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing large current switch electric energy meter detection method has insufficient detection accuracy, functional limitations, weak real-time calibration capability, and how to achieve high-precision error analysis and comprehensive performance detection under complex working conditions.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent verification and detection method for a large current switch built-in electric energy meter, comprising: inputting a signal to the electric energy meter under test; outputting the difference between the output data of the standard electric energy meter and the electric energy meter under test through a real-time error model; performing functional detection and dynamic self-calibration based on real-time analysis of the detection data.

[0007] As a preferred solution of the intelligent verification and detection method for a large current switch built-in electric energy meter described in the present invention, wherein: the input signal is connected to the electric energy meter under test, including generating a signal source, generating a standard test signal through a digital signal synthesis technology, including a voltage signal, a current signal and a harmonic signal;

[0008] inputting the generated standard test signal into the electric energy meter under test;

[0009] Connectors and shielded cables were used during testing.

[0010] As a preferred solution of the intelligent verification and detection method for a large current switch built-in electric energy meter described in the present invention, wherein: the input signal is connected to the electric energy meter under test, including filtering the collected original signal to remove random noise and interference signals introduced by the environment or equipment;

[0011] Use real-time Fourier transform to analyze the spectrum characteristics of the collected signal and detect the distortion of the signal;

[0012] Dynamically correct the amplitude, frequency and phase of the input signal according to the detection results;

[0013] Perform integrity check on the filtered signal, compare the processed signal with a standard reference signal, and verify the amplitude, phase, and frequency accuracy of the signal;

[0014] Normalize the collected voltage, current and power data to unify the signal dimensions;

[0015] Time-align all signals to eliminate time offsets caused by channel delays during the sampling process.

[0016] As a preferred solution of the intelligent verification and detection method for a large current switch built-in electric energy meter described in the present invention, wherein: the difference between the output data of the standard electric energy meter and the electric energy meter under test is output through the real-time error model, including based on the difference between the output data of the standard electric energy meter and the electric energy meter under test, using the real-time error model, outputting the error value of the electric energy meter under different load, voltage and current conditions, using the improved electric energy error algorithm, taking into account the influence of harmonics and frequency on the error;

[0017] An absolute error model is constructed to measure the numerical difference between the measured value of the electric energy meter under test and the reference value of the standard electric energy meter, which is expressed as:

[0018] E abs =M test -M std

[0019] Among them, E abs Represents the absolute error, Mtest Indicates the measured value output by the measured electric energy meter, M std Indicates the reference value provided by the standard electric energy meter;

[0020] A relative error model is constructed to measure the ratio of the error to the reference value and unify the error evaluation of measurements of different magnitudes, which is expressed as:

[0021]

[0022] Among them, E rel Relative error, M std Indicates the standard value, used for normalization;

[0023] Provide a unified error metric.

[0024] As a preferred solution of the intelligent verification and detection method for a large current switch built-in electric energy meter described in the present invention, wherein: the difference between the output data of the standard electric energy meter and the electric energy meter under test is output through the real-time error model, including constructing a multi-dimensional error output model, expanding the error output, and being applicable to the influencing factors of multiple variables, including voltage, current, frequency, and phase;

[0025] By decomposing the error, the deviations under different signal parameters are calculated respectively, which can be expressed as:

[0026]

[0027] Among them, E i (V, I, f, φ) represents multi-dimensional error, reflecting the deviation under the parameter combination. V represents the input voltage value, I represents the input current value, f represents the input signal frequency, φ represents the signal phase, and M ... current value, f represents the input signal frequency, φ represents the signal phase, and M represents the input voltage value test (V, I, f, φ) represents the measured value of the measured electric energy meter under specific working conditions, M std (V, I, f, φ) represents the reference value of the standard electric energy meter under the same working conditions;

[0028] By constructing a multi-dimensional error output model, accurate error analysis of test conditions can be achieved, and multi-parameter combination verification is supported.

[0029] As a preferred solution of the intelligent verification and detection method for a large current switch built-in electric energy meter described in the present invention, wherein: the function detection and dynamic self-calibration based on real-time analysis of detection data include starting and creeping tests, fee control function detection and communication function detection;

[0030] The starting and creeping tests simulate the small current state to verify the starting performance and creeping state of the electric energy meter under test;

[0031] The fee control function test tests the fee control response capability of the tested electric energy meter by simulating a trip signal;

[0032] The communication function test tests the infrared, RS485 and carrier communication capabilities of the electric energy meter under test, and verifies the accuracy and real-time performance of the communication.

[0033] As a preferred solution of the intelligent verification and detection method for a large current switch built-in electric energy meter described in the present invention, the function detection and dynamic self-calibration based on real-time analysis of detection data include constructing an error linear superposition model, decomposing the multidimensional error into a linear superposition of voltage, current, frequency, and phase, quantifying the contribution of each parameter to the total error, and determining the weight of each parameter through a multivariate fitting method, which is expressed as:

[0034] E total =E V +E I +E f +E φ

[0035] Among them, E total Represents the total error, taking all influencing factors into consideration, E V Represents voltage error, E I Represents the current error, E f Represents the frequency error, E φ represents the phase error;

[0036] Locate the source of error and guide subsequent calibration;

[0037] The harmonic distortion factor measures the impact of signal harmonics on the error. The harmonic error is taken as an additional item, and its contribution is controlled by the weight factor. The harmonic error and total error model is constructed as follows:

[0038]

[0039] E final =E total +α·THD

[0040] THD stands for total harmonic distortion, which reflects the ratio of harmonic amplitude to fundamental wave. n Represents the amplitude of the nth order harmonic, V 1 Indicates the amplitude of the fundamental wave, E final represents the final error, including the total error and harmonic influence, α represents the weight factor of harmonic influence, which is determined according to experimental data;

[0041] Combine the error calculation results to generate error curves and electric energy meter performance reports;

[0042] According to the deviation of the error curve, the operating status and potential faults of the measured electric energy meter can be intelligently judged.

[0043] Another object of the present invention is to provide an intelligent calibration and detection system for a large current switch built-in electric energy meter, which can output the difference between the output data of the standard electric energy meter and the electric energy meter under test through a real-time error model, thereby solving the problem of insufficient detection accuracy in the current large current switch electric energy meter detection method.

[0044] As a preferred solution of the intelligent verification and detection system for large-current switch built-in electric energy meters described in the present invention, it includes: an initialization module, an error output module, and a dynamic self-calibration module; the initialization module is used to collect data and perform preprocessing; the error output module is used to output the error value of the tested electric energy meter under different load, voltage and current conditions based on the difference in output data between the standard electric energy meter and the tested electric energy meter, using a real-time error output model; the dynamic self-calibration module is used to adjust the test signal parameters by real-time analysis of the detection data.

[0045] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an intelligent verification and detection method for a large current switch built-in electric energy meter.

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an intelligent verification and detection method for a large-current switch built-in electric energy meter.

[0047] Beneficial effects of the present invention: The intelligent verification and detection method for a large current switch built-in electric energy meter provided by the present invention constructs a real-time error model, calculates the error value under different working conditions based on the output data difference between the standard electric energy meter and the electric energy meter under test, provides a quantitative analysis of the precise error of the electric energy meter under test, and significantly improves the real-time and accuracy of the detection. Through the absolute error and relative error models, the error evaluation of measurement values ​​of different magnitudes is unified, the complexity of multi-condition data analysis is simplified, and a standardized basis is provided for error comparison and evaluation. Through the multidimensional error model, the parameter influence is decomposed, and multi-variable combination verification is supported, which can accurately quantify the contribution of each parameter to the error and provide guidance for the design optimization of the electric energy meter. The function detection module is used to detect the starting, creeping, fee control function and communication function, to ensure the versatility and performance stability of the electric energy meter, and to provide a guarantee for reliable operation in actual application scenarios. Through the dynamic self-calibration mechanism, the error linear superposition model is constructed using real-time data to improve the intelligence and dynamic adaptability of the detection, and significantly reduce the impact of environmental interference on the detection results. By introducing the harmonic distortion factor, a harmonic error and total error model is constructed to ensure the accuracy of error analysis under conditions with significant harmonic distortion, thereby improving the applicability of electric energy meter detection in modern power grids. The present invention achieves better results in terms of detection accuracy and adaptability, as well as the system's ability to adapt to complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 The first embodiment of the present invention provides an overall flow chart of an intelligent verification and detection method for a large current switch built-in electric energy meter.

[0050] Figure 2 The electrical schematic diagram of a device for an intelligent calibration and detection system of a large current switch built-in electric energy meter provided in the third embodiment of the present invention.

[0051] Figure 3 A signal source diagram of an intelligent calibration and detection system for a high-current switch built-in electric energy meter provided in the third embodiment of the present invention.

[0052] Figure 4 A principle block diagram of an error calculator for an intelligent calibration and detection method for a high-current switch built-in electric energy meter provided in the first embodiment of the present invention.

[0053] Figure 5A block diagram of the principle of a standard clock reference for an intelligent verification and detection method for a high-current switch built-in electric energy meter provided in the first embodiment of the present invention.

[0054] Figure 6 A communication unit diagram of an intelligent verification and detection method for a large current switch built-in electric energy meter provided in the first embodiment of the present invention.

[0055] Figure 7 The third embodiment of the present invention provides an overall flow chart of an intelligent calibration and detection system for a large current switch built-in electric energy meter. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0057] Example 1, reference Figure 1-Figure 6 , which is an embodiment of the present invention, provides an intelligent verification and detection method for a large current switch built-in electric energy meter, comprising:

[0058] S1: The input signal is connected to the electric energy meter under test.

[0059] Furthermore, the generation of high-precision signal sources. Using high-performance ARM chips (such as STM32F207) and FPGA to work together, standard test signals are generated through digital signal synthesis technology, including:

[0060] Voltage signal: simulate power supply conditions of different voltage levels (such as 220V, 110V, etc.), and the amplitude adjustment accuracy reaches 0.01V;

[0061] Current signal: Output current signals of different ranges (such as 10A to 1000A) to support simulation of load change conditions;

[0062] Harmonic signal: Inject harmonic signals of various orders (such as 2nd to 50th) according to the actual working conditions of the electric energy meter to realize the simulation test of harmonic distortion.

[0063] Signal generation supports:

[0064] Waveform type: sine wave, square wave, peak wave, thyristor wave, etc.

[0065] Frequency range: supports wide frequency output from 0.1Hz to 1kHz to meet the needs of power frequency and harmonic testing;

[0066] Phase Adjustment: Generate precise multiphase signals through digital phase shifting technology.

[0067] The input signal transmission is connected to the electric energy meter under test, and the generated standard test signal is input to the electric energy meter under test through the high-precision transmission module.

[0068] During the test, high-stability connectors and shielded cables are used to ensure that the signal transmission is not affected by external interference.

[0069] Multi-channel data acquisition equipment, equipped with a high-precision analog-to-digital converter (ADC), with a sampling accuracy of up to 18 bits and a sampling frequency of 100kS / s, supporting multi-channel parallel acquisition.

[0070] Real-time acquisition of output signals of the electric energy meter under test, including: voltage signal, current signal, active power signal, reactive power signal, pulse output signal, etc.

[0071] Multi-channel design supports simultaneous testing of multiple energy meters.

[0072] It should be noted that signal filtering is the process of filtering the collected original signal to remove random noise and interference signals introduced by the environment or equipment to ensure the purity of the data:

[0073] Low-pass filtering: remove high-frequency interference signals and retain effective power signals;

[0074] Bandpass filtering: retains signals in a specific frequency range (such as 50Hz and its multiples);

[0075] Adaptive filtering: Dynamically adjust the filter parameters to remove specific noise based on the spectral characteristics of the input signal.

[0076] Waveform distortion correction uses real-time Fourier transform (FFT) to analyze the spectrum characteristics of the collected signal and detect the distortion of the signal (such as total harmonic distortion THD).

[0077] According to the test results, the amplitude, frequency and phase of the input signal are dynamically corrected to ensure that the signal output always meets the requirements of standard working conditions:

[0078] Amplitude correction: fine-tune the voltage and current amplitudes to compensate for attenuation during transmission;

[0079] Frequency correction: calibrate the slight drift of the input signal frequency to ensure that it is consistent with the target value;

[0080] Phase correction: Through digital phase shifting technology, the phase offset is adjusted to ensure the phase consistency between the input signal and the collected signal.

[0081] Signal integrity check: perform integrity check on the filtered signal to ensure that the signal meets the standard requirements in both time and frequency domains;

[0082] The processed signal is compared with a standard reference signal to verify the signal amplitude, phase and frequency accuracy.

[0083] Data standardization processing: normalize the collected voltage, current, and power data to unify the signal dimensions, which is convenient for subsequent error calculation and analysis;

[0084] Time-align all signals to eliminate time offsets caused by channel delays during the sampling process.

[0085] S2: The difference between the output data of the standard electric energy meter and the electric energy meter under test is output through the real-time error model.

[0086] Furthermore, based on the difference in output data between the standard energy meter and the energy meter under test, a real-time error model is used to output the error value of the energy meter under test under different load, voltage and current conditions, and an improved energy error algorithm is used to take into account the influence of harmonics and frequency on the error;

[0087] An absolute error model is constructed to measure the numerical difference between the measured value of the electric energy meter under test and the reference value of the standard electric energy meter, which is expressed as:

[0088] E abs =M test -M std

[0089] Among them, E abs Represents the absolute error, M test Indicates the measured value output by the measured electric energy meter, M std Indicates the reference value provided by the standard electric energy meter;

[0090] A relative error model is constructed to measure the ratio of the error to the reference value and unify the error evaluation of measurements of different magnitudes, which is expressed as:

[0091]

[0092] Among them, E rel Relative error, M std Indicates the standard value, used for normalization;

[0093] Provide a unified error metric.

[0094] It should be noted that the multi-dimensional error output model is constructed to expand the error output and is applicable to the influencing factors of multiple variables, including voltage, current, frequency, and phase;

[0095] By decomposing the error, the deviations under different signal parameters are calculated respectively, which can be expressed as:

[0096]

[0097] Among them, E i (V, I, f, φ) represents multi-dimensional error, reflecting the deviation under the parameter combination. V represents the input voltage value, I represents the input current value, f represents the input signal frequency, φ represents the signal phase, and M ... current value, f represents the input signal frequency, φ represents the signal phase, and M represents the input voltage value test (V, I, f, φ) represents the measured value of the measured electric energy meter under specific working conditions, M std (V, I, f, φ) represents the reference value of the standard electric energy meter under the same working conditions;

[0098] By constructing a multi-dimensional error output model, accurate error analysis of test conditions can be achieved, and multi-parameter combination verification is supported.

[0099] S3: Perform functional testing and dynamically self-calibrate based on real-time analysis of test data.

[0100] Furthermore, start-up and creep tests, fee control function tests and communication function tests are carried out;

[0101] The starting and creeping tests simulate the small current state to verify the starting performance and creeping state of the electric energy meter under test;

[0102] The fee control function test tests the fee control response capability of the tested electric energy meter by simulating a trip signal;

[0103] The communication function test tests the infrared, RS485 and carrier communication capabilities of the electric energy meter under test, and verifies the accuracy and real-time performance of the communication.

[0104] It should be noted that the error linear superposition model is constructed to decompose the multidimensional error into the linear superposition of voltage, current, frequency, and phase, quantify the contribution of each parameter to the total error, and determine the weight of each parameter through the multivariate fitting method, which is expressed as:

[0105] E total =E V +E I +E f +E φ

[0106] Among them, E total Represents the total error, taking all influencing factors into consideration, E V Represents voltage error, E I Represents the current error, E f Represents the frequency error, E φ represents the phase error;

[0107] Locate the source of error and guide subsequent calibration;

[0108] The harmonic distortion factor measures the impact of signal harmonics on the error. The harmonic error is taken as an additional item, and its contribution is controlled by the weight factor. The harmonic error and total error model is constructed as follows:

[0109]

[0110] E final =E total +α·THD

[0111] THD stands for total harmonic distortion, which reflects the ratio of harmonic amplitude to fundamental wave. n Represents the amplitude of the nth order harmonic, V 1 Indicates the amplitude of the fundamental wave, E final represents the final error, including the total error and harmonic influence, α represents the weight factor of harmonic influence, which is determined according to experimental data;

[0112] Combine the error calculation results to generate error curves and electric energy meter performance reports;

[0113] According to the deviation of the error curve, the operating status and potential faults of the measured electric energy meter can be intelligently judged.

[0114] Furthermore, the large current switch built-in electric energy meter calibration device is composed of an integrated digital synthetic signal source, a high-stability power source, an overload automatic protection circuit, a current and voltage output conversion circuit, a multi-functional standard electric energy meter, a standard clock reference, a distributed error calculator, etc.

[0115] The calibration device uses advanced digital signal synthesis technology, with a powerful ARM as the core, using digital synthesis, digital frequency modulation, digital phase shift, digital amplitude modulation and other technologies, and obtains accurate small voltage analog signals through active low-pass filtering, and then outputs large voltage and current signals through the power amplifier circuit. The reliable design of the calibration device ensures that the output voltage, current, phase, and frequency have satisfactory setting accuracy and adjustment fineness, and ensures that the device has high output stability and low distortion.

[0116] It should be noted that the entire detection device is mainly composed of a signal source, a power amplifier, a standard electric energy meter, a standard clock reference, an error calculator, a function detection unit, a communication unit, a PC and a meter stand. The internal components of the device exchange information through the RS232 / 485 bus.

[0117] The signal source uses the ARM high-speed digital processing chip STM32F207 as the main control CPU. One chip is used as the human-machine interface, responsible for LCD display, keyboard operation, parameter setting, gear switching and communication with the PC, and the other chip is used for waveform generation. The waveform generation part of the ARM chip (CPU) continuously sends data to the DA conversion chip in real time and at high speed according to the human-machine interface command to synthesize the required various waveforms (sine wave, thyristor waveform, subharmonic, various content harmonics, square wave and peak wave, etc.) to meet the various harmonic and waveform test requirements of IR46.

[0118] The power amplifier adopts a linear power amplifier with high stability in voltage, current and power output, small waveform output distortion, and has the functions of automatic detection, alarm and protection of voltage and current output failures.

[0119] The standard electric energy meter adopts the latest international digital electronic technology, with STM32F207, 32-bit ARM high-speed processor as the core, and adds various advanced means such as active compensation technology of mutual inductor, 18-bit high-speed AD conversion technology, and digital compensation technology, so that the meter can achieve a high accuracy of better than 0.02% RD relative error. The standard meter can simultaneously measure and display voltage (V), current (A), active power (W), reactive power (Var), frequency (Hz), power factor (PF), phase angle (θ), apparent power (S) and other electrical quantities, and has functions such as harmonic analysis, bar graph display and error test, and can also output electric energy pulses (active and reactive power are optional), which is convenient for inspection or providing standard pulses for other devices.

[0120] The error calculator is the core part of the error detection and function detection of the entire calibration device. The main control chip uses ST's STM32F103 chip. With the peripheral interface circuit, the error calculator has the functions of power error calculation, clock error calculation, power meter start and creep test, open circuit detection, fee control trip detection and 485 communication. The error calculator principle block diagram is as follows Figure 4 .

[0121] The clock standard source uses a quartz crystal oscillator with a frequency of 10MHz as the standard, with an accuracy of 10-8 and a stability of 10-9. The main control CPU uses STM32F103, which is mainly responsible for parameter setting, communication, and time display. In order to reduce the interference of high-frequency signals and the working ability of the timing counter of the error calculator itself, the clock reference 10MHz signal needs to be distributed and processed, and the frequency output can be selected to be 500KHz or 100KHz. The principle block diagram is as follows Figure 5 .

[0122] The serial port server is used for serial port, which can realize infrared and 485 one-to-one communication of the electric energy meter under test. The carrier communication of the meter under test can be carried out through the carrier switching box. The specific configuration is as follows:

[0123] a)Serial port server: 1 channel 32 ports.

[0124] b) Each table provides one infrared communication interface;

[0125] c) Each table provides two independent 485 communication ports, one-to-one communication mode;

[0126] d) Equipped with 1 set of carrier communication switching unit, which can switch between five carrier modules.

[0127] Input and output interface:

[0128] a) Each epitope provides an optoelectronic sampler input interface;

[0129] b) Each meter provides a simultaneous input interface for multiple signals such as active power, reactive power, multi-function, tripping, etc.;

[0130] c) The input circuit has a high-voltage isolation circuit to prevent high voltage from being accidentally connected to damage the error calculator;

[0131] d) It is compatible with different pulse input voltages and has a shaping function for the input sampling pulse.

[0132] Embodiment 2, an embodiment of the present invention, provides an intelligent verification and detection method for a large current switch built-in electric energy meter. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0133] First, the experiment aims at the calibration and detection of large current switch energy meters, and uses the comparison method between the standard energy meter and the energy meter under test to verify the innovation of the invention. The experimental equipment is configured, including a signal generator, a standard energy meter, an energy meter under test, a test connector and a shielded cable. The signal generator adopts digital signal synthesis technology, sets the output parameters to voltage 220V, current 10A, frequency 50Hz, and superimposes controllable harmonic signals. The standard signal is input into the energy meter under test using a shielded cable. After the signal is collected, it is filtered to remove noise and interference, and the signal spectrum characteristics are analyzed by real-time Fourier transform to detect distortion. According to the test results, the amplitude, frequency and phase of the signal are dynamically corrected to ensure signal integrity. A real-time error model is constructed to compare the differences in parameters such as voltage, current, and power output by the standard energy meter and the energy meter under test. An improved error algorithm is used to consider the influence of harmonic distortion on the measurement results, and the absolute error and relative error are calculated. Conduct starting and creeping tests to verify the starting sensitivity and creeping state of the energy meter under test; simulate tripping signals to test the response time and accuracy of the fee control function; test RS485, infrared and carrier communication performance to verify the stability of data transmission. Record the measurement data of multiple tests of the standard energy meter and the energy meter under test, and compare the results of the improved measurement system.

[0134] Table 1 Experimental data table

[0135]

[0136] In the unmodified system, the errors of voltage, current and power measurement reached 0.23% and -0.14% respectively. These errors mainly come from input signal distortion and insensitive response of the equipment. In the improved system, the errors were significantly reduced to 0.09% and 0.00%, proving that the dynamic calibration technology effectively improved the detection accuracy. The improved system significantly reduced the impact of harmonic interference on the measurement results through real-time Fourier transform and harmonic distortion factor calculation. Under complex working conditions, the amplitude and frequency accuracy of the signal are higher than those of the unimproved system, verifying the robustness of the model. The starting and creeping test data show that the measured energy meter can respond quickly under low current state and there is no creeping phenomenon. The fee control function test shows that the response time of tripping and power restoration meets the design requirements. The communication test results show that the improved system has stable data transmission and no packet loss in the three communication modes of infrared, RS485 and carrier. Through real-time error model and multi-dimensional error analysis, the problem that the traditional system cannot take into account the errors of multiple variables (voltage, current, frequency, phase) at the same time is solved. Dynamic self-calibration technology further improves signal integrity and system adaptability, providing an innovative solution for high-precision electricity meter detection under complex working conditions.

[0137] In summary, the experimental data fully proves the innovation and practicality of the present invention. Its improved technical solution effectively makes up for the deficiencies of the existing technology in error control and function detection, and provides new ideas and methods for the electric energy meter detection industry.

[0138] Example 3, reference Figure 7 , which is an embodiment of the present invention, provides an intelligent verification and detection system for a large current switch built-in electric energy meter, including an initialization module, an error output module, and a dynamic self-calibration module.

[0139] The initialization module is used to collect data and perform preprocessing. The error output module is used to output the error value of the tested electric energy meter under different load, voltage and current conditions based on the difference in output data between the standard electric energy meter and the tested electric energy meter, using a real-time error output model. The dynamic self-calibration module is used to adjust the test signal parameters by real-time analysis of the detection data.

[0140] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0141] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0142] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0143] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent verification and detection method for a large current switch built-in electric energy meter, characterized in that: include: The input signal is connected to the electric energy meter under test; The difference between the output data of the standard electric energy meter and the electric energy meter under test is output through the real-time error model; Perform functional testing and dynamically self-calibrate based on real-time analysis of test data.

2. The intelligent verification and detection method for a large current switch built-in electric energy meter according to claim 1, characterized in that: The input signal is connected to the electric energy meter under test, including generating a signal source, generating a standard test signal through a digital signal synthesis technology, including a voltage signal, a current signal and a harmonic signal; inputting the generated standard test signal into the electric energy meter under test; Connectors and shielded cables were used during testing.

3. The intelligent verification and detection method for a large current switch built-in electric energy meter according to claim 2, characterized in that: The input signal is connected to the measured electric energy meter, including filtering the collected original signal to remove random noise and interference signals introduced by the environment or equipment; Use real-time Fourier transform to analyze the spectrum characteristics of the collected signal and detect the distortion of the signal; Dynamically correct the amplitude, frequency and phase of the input signal according to the detection results; Perform integrity check on the filtered signal, compare the processed signal with a standard reference signal, and verify the amplitude, phase, and frequency accuracy of the signal; Normalize the collected voltage, current and power data to unify the signal dimensions; Time-align all signals to eliminate time offsets caused by channel delays during the sampling process.

4. The intelligent verification and detection method for a large current switch built-in electric energy meter according to claim 3, characterized in that: The method of outputting the difference between the output data of the standard electric energy meter and the electric energy meter under test by the real-time error model includes: based on the difference between the output data of the standard electric energy meter and the electric energy meter under test, using the real-time error model to output the error value of the electric energy meter under test under different load, voltage and current conditions, using the improved electric energy error algorithm, and taking into account the influence of harmonics and frequency on the error; An absolute error model is constructed to measure the numerical difference between the measured value of the electric energy meter under test and the reference value of the standard electric energy meter, which is expressed as: E abs =M test -M std Among them, E abs Represents the absolute error, M test Indicates the measured value output by the measured electric energy meter, M std Indicates the reference value provided by the standard electric energy meter; A relative error model is constructed to measure the ratio of the error to the reference value and unify the error evaluation of measurements of different magnitudes, which is expressed as: Among them, E rel Relative error, M std Indicates the standard value, used for normalization; Provide a unified error metric.

5. The intelligent verification and detection method for a large current switch built-in electric energy meter according to claim 4, characterized in that: The output of the difference between the output data of the standard electric energy meter and the electric energy meter under test by the real-time error model includes constructing a multi-dimensional error output model, expanding the error output, and being applicable to the influencing factors of multiple variables, including voltage, current, frequency, and phase; By decomposing the error, the deviations under different signal parameters are calculated respectively, which can be expressed as: Among them, E i (V, I, f, φ) represents multi-dimensional error, reflecting the deviation under the parameter combination. V represents the input voltage value, I represents the input current value, f represents the input signal frequency, φ represents the signal phase, and M ... test (V, I, f, φ) represents the measured value of the measured electric energy meter under specific working conditions, M std (V, I, f, φ) represents the reference value of the standard electric energy meter under the same working conditions; By constructing a multi-dimensional error output model, accurate error analysis of test conditions can be achieved, and multi-parameter combination verification is supported.

6. The intelligent verification and detection method for a large current switch built-in electric energy meter according to claim 5, characterized in that: The function detection and dynamic self-calibration based on real-time analysis of detection data include starting and creeping tests, fee control function detection and communication function detection; The starting and creeping tests simulate the small current state to verify the starting performance and creeping state of the electric energy meter under test; The fee control function test tests the fee control response capability of the tested electric energy meter by simulating a trip signal; The communication function test tests the infrared, RS485 and carrier communication capabilities of the electric energy meter under test, and verifies the accuracy and real-time performance of the communication.

7. The intelligent verification and detection method for a large current switch built-in electric energy meter according to claim 6, characterized in that: The function detection and dynamic self-calibration based on real-time analysis of detection data include constructing an error linear superposition model, decomposing the multi-dimensional error into the linear superposition of voltage, current, frequency, and phase, quantifying the contribution of each parameter to the total error, and determining the weight of each parameter through a multivariate fitting method, which is expressed as: AND total =And V +E I +E f +E φ Among them, E total Represents the total error, taking all influencing factors into consideration, E V Represents voltage error, E I Represents the current error, E f Represents the frequency error, E φ represents the phase error; Locate the source of error and guide subsequent calibration; The harmonic distortion factor measures the impact of signal harmonics on the error. The harmonic error is taken as an additional item, and its contribution is controlled by the weight factor. The harmonic error and total error model is constructed as follows: E final =E total +α·THD THD stands for total harmonic distortion, which reflects the ratio of harmonic amplitude to fundamental wave. n represents the amplitude of the nth order harmonic, V1 represents the amplitude of the fundamental wave, E final represents the final error, including the total error and harmonic influence, α represents the weight factor of harmonic influence, which is determined according to experimental data; Combine the error calculation results to generate error curves and electric energy meter performance reports; According to the deviation of the error curve, the operating status and potential faults of the measured electric energy meter can be intelligently judged.

8. A system using the intelligent verification and detection method for a large current switch built-in electric energy meter as claimed in any one of claims 1 to 7, characterized in that: Including initialization module, error output module, dynamic self-calibration module; The initialization module is used to collect data and perform preprocessing; The error output module is used to output the error value of the measured electric energy meter under different load, voltage and current conditions by using a real-time error output model based on the difference between the output data of the standard electric energy meter and the measured electric energy meter; The dynamic self-calibration module is used to adjust the test signal parameters by analyzing the detection data in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent verification and detection method for a large current switch built-in electric energy meter according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent verification and detection method for a large current switch built-in electric energy meter according to any one of claims 1 to 7 are implemented.

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