Online metering monitoring method for automatic verification system of electric energy metering instrument

Abstract

The invention relates to the technical field of metering monitoring, and discloses an on-line metering monitoring method of an automatic verification system of an electric energy metering instrument, the verification system provides on-line quantity value traceability data to the automatic verification system, and the verification system provides on-line verification data to the automatic verification system; the automatic verification system ensures that a scheme executes real-time online monitoring on the basis of the data, big data analysis of machine learning and calculation of a quality control chart; the high-precision electric energy meter is used for measuring the primary current and the secondary current, and a precise voltage and current sampling device and high-precision mutual inductor data are used, so that the monitoring accuracy can be improved. And through real-time online monitoring of the automatic verification system, online quantity value traceability data and online verification data can be rapidly acquired and analyzed, so that the monitoring efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of metering and monitoring, and in particular to an online metering and monitoring method for an automatic verification system of an electric energy metering instrument. Background Art

[0002] In addition to regular weekly inspections, the automated verification system for electric energy metering instruments is online for a long time and operates at high load. In order to ensure the accuracy and reliability of its metering performance, it is usually necessary to conduct periodic verification at a certain frequency. However, traditional offline verification methods, such as sample verification method, transfer comparison method, and inter-laboratory comparison method, require the verification system to be shut down, with low verification efficiency and unable to reflect the actual operating conditions of the verification system. The verification quality is generally not high. Regarding the periodic verification method of the verification system, Fan Qiaocheng [1,2] proposed to use the electric energy meter sub-meter to realize real-time verification of the main meter. The verification standard of this method cannot be transferred to the verification station. Du Shuwei [3] used the Bayesian hierarchical model to simulate a high-accuracy virtual standard electric energy meter to realize online verification of the verification system. Zhuang Lei et al. [4,5] developed a verification standard that can be automatically transferred to the verification system to realize online verification of any station of the verification system.

[0003] The above achievements have realized the online verification of a certain station of the calibration system, but none of them have studied the verification cycle, the number of verification standards, etc., such as the cost of false detection and misdetection caused by too low a verification frequency, the waste of resources caused by too high a verification frequency, and the impact of verification during the implementation period on the verification efficiency and capacity ratio (CUR) of the calibration system. In practice, the verification cycle usually depends on the experience of the test personnel, and regular verification is carried out at a fixed time interval, or irregular verification is carried out for a certain model or a certain batch. Therefore, it is difficult to quantitatively predict the verification cycle, and there is a lack of relevant theoretical basis. One method is to refer to the prediction method of the calibration cycle. Common prediction methods for the calibration cycle include gray neural network method, statistical method, reliability method, step diagram method, Taguchi method, maximum likelihood estimation method, Bayesian method, etc. The gray neural network model and particle swarm optimization support vector machine model proposed by Yao Gang and Yue Ruihua are suitable for situations with little data. Wang Jing and Morello R used historical data to establish a reliability model. Wang Rui

[12] proposed a prediction method that combines reliability and maximum likelihood estimation. Zhu Lifeng and Zhang Yueyi used Taguchi-style measurement quality engineering as a tool and focused on economic indicators. The above research results have made quantitative predictions on the calibration cycle from different aspects such as statistics, reliability, and economics, but they lack the coordinated optimization of multiple objectives such as quality, cost, and efficiency. In particular, there is no research on the impact of verification during implementation on calibration efficiency and production capacity ratio. In terms of solving multi-objective optimization models, recent studies have mostly adopted the non-dominated sorting method

[15] to obtain a set of Pareto solutions as the optimal solution set. The NSGA-II algorithm proposed by Long and Liu Bin [16,17] and the particle swarm algorithm based on the speed exchange mechanism improve the search traversal and prevent the population from falling into the local optimum. The NSGA-III algorithm, multi-subpopulation and density estimation algorithm proposed by Bhesdadiya, Wang Heping, and Zhao Zhiwei [18-20] have good distribution and convergence accuracy in high-dimensional multi-objective optimization. Although a lot of research has been done in the existing literature, the operating conditions of the verification system are complex, and multiple constraints are required for a series of problems such as false detection, wrong detection, maintenance, cost, efficiency, and production capacity, and the solution space is large. When dealing with multi-dimensional and multiple constraints, the traditional NSGA-II algorithm has low evolution efficiency and poor solution distribution, and is prone to fall into local optimality.

[0004] Regarding the automatic verification system of electric energy metering instruments, the foreign power system is different from that in China. Large-scale centralized verification has not been carried out, and there is no such metering device as the automatic verification system of electric energy metering instruments. However, for the value assurance or quality control of intelligent factories or highly integrated industrial automatic systems, foreign countries usually adopt the measurement assurance program (MAP Measurement Assurance Program). The measurement assurance program (MAP) is one of the technical measures currently used internationally to ensure the integrity of the value transfer system. The electric energy measurement assurance program (EMAP) is the specific application of the measurement assurance program (MAP) in the electric energy value transfer system. Its core technology is to collect data through regular period verification and value comparison, use the theory and methods of probability theory (probability) and mathematical statistics (statistics), establish corresponding mathematical models, and analyze and evaluate the verified and compared measurement data, and then judge the meter detection capability and level based on the evaluation results. There is also a measurement assurance program (MAP).

[0005] In addition, remote calibration is similar to the online calibration technology route to a certain extent. Remote calibration technology is an advanced technology developed on the basis of traditional calibration technology, which integrates the application of instrumentation, intelligent detection, computer and other disciplines. It enables the measurement instruments or systems to be directly traced back to the national master reference or the measurement standards of the metrology technical institutions at all levels through road transportation, shortening the calibration time and reducing the calibration cost. At the same time, it can also expand the measurement assurance plan, expand the calibration capacity of the national metrology laboratory, and improve the calibration level of the lower-level metrology laboratory.

[0006] In foreign countries, remote calibration technology is currently a hot topic in the field of metrology. Most of them are realized through the medium of transfer standards, which is roughly the same as online traceability. Metrology institutions and technicians in various countries have carried out a lot of research in the early stage and developed various types of remote calibration systems. The National Physical Laboratory of the United Kingdom sent the calibrated high-precision physical transfer standards to the customer site, and completed the remote calibration of standard resistance and voltage and the traceability analysis of network analyzers with the help of the network. In order to carry out calibration services for large-flow gas flowmeters, the National Institute of Standards and Technology of the United States transformed the "A Benchmark" of the Colorado Engineering Laboratory and upgraded it to the highest national standard, realizing the function of remote calibration.

[0007] The National Metrology Institute of Japan and the Japan Industrial Science and Technology Development Institute have carried out more than ten remote calibration projects including Josephson voltage, optics, frequency, pressure, length, and temperature. The German Federal Institute of Physics and Technology has realized the remote online calibration task of polarization neutron diffractometer by using a well-designed physical transfer standard.

[0008] Italian scholar Alessio Carullo designed a data acquisition board calibration transfer standard to achieve remote calibration of industrial field data acquisition boards. He also emphasized the security issues when exchanging sensitive information for remote calibration via the Internet. The Egyptian National Institute of Standards (NIS) introduced a fully automated system for remote calibration of resistance testers, which is mainly used for routine calibration of low-precision calibration.

[0009] In summary, in order to realize the online measurement supervision of the automatic verification system of electric energy metering instruments, the development of transmission standards is crucial. First, the high-precision electric energy metering standard device must be miniaturized and lightweight. At present, the electric energy metering standard devices of the same accuracy level are too large in size and weight, and cannot meet the online verification requirements of the automatic verification system. Secondly, the automatic verification system of electric energy metering instruments is a measurement standard device with high accuracy, complex measurement process, and important measurement results. It is also easily affected by environmental factors such as electromagnetic interference and mechanical vibration. The traditional offline verification method cannot directly reflect the actual operating conditions of the verification system, and the verification effect is poor. Summary of the invention

[0010] Technical problems to be solved: In order to solve the above problems, it is of great significance to develop a miniaturized high-precision electric energy metering standard device and a multi-objective optimization verification algorithm and a real-time online monitoring method for multi-state comprehensive variables.

[0011] Technical Solution

[0012] An online measurement monitoring method for an automated verification system of an electric energy metering instrument, wherein the verification system provides online value traceability data to the automated verification system, and the verification system provides online verification data to the automated verification system. The automated verification system ensures online value traceability and verification of the verification system under the calculation of the above data, big data analysis results of machine learning, and quality control procedures, and finally performs real-time online monitoring;

[0013] The online value traceability data of the verification system includes high-precision electric energy meter data, precision voltage and current sampling device data, temperature self-compensated voltage basic quasi-circuit data, high-precision mutual inductor data, magnet material performance data, cross-compensation shielding data, and standard data preset by the verification system;

[0014] The online verification data of the calibration system includes data for constructing a standard library for multi-type faults and high stability verification, data for constructing a verification mathematical model for a multi-objective optimization algorithm, data for solving the verification model using a hybrid variable neighborhood algorithm, and data for the optimal verification solution for the calibration system.

[0015] The big data analysis of machine learning and the calculation of quality control charts include data mining, feature extraction and pattern recognition of online measurement traceability data and online verification data.

[0016] The verification mathematical model data of the multi-objective optimization algorithm quantifies multiple indicators of the electric energy metering device, and solves the optimal verification solution through the optimization algorithm.

[0017] The hybrid variable neighborhood algorithm solves the verification model through a variety of variable neighborhood search strategies to obtain the optimal verification solution.

[0018] The high-precision electric energy meter also includes a voltage channel, a resistor voltage division sampling, a current channel, a current transformer sampling, a dual-channel fully differential synchronous ADC converter, a DSP data processing module, a frequency division, a low pulse and a high pulse device.

[0019] The online value traceability device of the verification system includes a high-precision electric energy meter, a precision voltage and current sampling device, a temperature self-compensating voltage basic circuit, a high-precision mutual inductor, magnet material properties, cross-compensation shielding, and a storage device for pre-set standards of the verification system.

[0020] The online verification device of the verification system includes a storage device for building a multi-type fault and high-stability verification standard library, a verification mathematical model module for building a multi-objective optimization algorithm, a hybrid variable neighborhood algorithm for solving the verification model, and a verification system optimal verification solution module.

[0021] The present invention also proposes a computer device, characterized in that it includes: one or more processors; the processor is used to execute one or more programs; when the one or more programs are executed by the one or more processors, an online metering monitoring method for an automatic calibration system of an electric energy metering instrument is implemented.

[0022] The present invention also provides a computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed, an online metering monitoring method for an automatic verification system of an electric energy metering instrument is implemented.

[0023] Beneficial Effects

[0024] The advantages of the present invention are: improving monitoring accuracy: by using a high-precision electric energy meter to measure the primary current and the secondary current, and using a precise voltage and current sampling device and high-precision mutual inductor data, the monitoring accuracy can be improved.

[0025] Improve monitoring efficiency: Through real-time online monitoring of the automated calibration system, online traceability data and online verification data can be quickly acquired and analyzed, thereby improving monitoring efficiency.

[0026] Guaranteeing accuracy and stability: The accuracy and stability of electric energy metering equipment can be guaranteed through big data analysis of machine learning and calculation of quality control charts, as well as solving the verification model using multi-objective optimization algorithms and hybrid variable neighborhood algorithms.

[0027] Optimal verification plan: By using the standard data pre-set by the calibration system and constructing a standard library data for multi-type faults and high stability verification, combined with the verification mathematical model data of the multi-objective optimization algorithm and the hybrid variable neighborhood algorithm to solve the verification model data, the optimal verification plan can be obtained to further improve the accuracy and efficiency of monitoring.

[0028] In summary, this online metering monitoring method can improve the monitoring accuracy and efficiency of electric energy metering instruments, ensure their precision and stability, and derive the optimal verification scheme, which has significant beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the implementation route of the present invention;

[0030] Figure 2 This is a principle block diagram of the electric energy meter of the present invention;

[0031] Figure 3 This is the T-type equivalent circuit of the current transformer of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Example 1 Please refer to Figure 1-3, an online metering monitoring method for an automated calibration system of an electric energy metering instrument, the calibration system provides online value traceability data to the automated calibration system, and at the same time the calibration system provides online verification data to the automated calibration system, the automated calibration system performs real-time online monitoring based on the above data and the big data analysis of machine learning and the calculation of quality control charts; the online value traceability data of the calibration system includes high-precision electric energy meter data, precision voltage and current sampling device data, temperature self-compensated voltage-based quasi-circuit data, high-precision mutual inductor data, magnet material performance data, cross-compensation shielding data and standard data pre-set by the calibration system; the online verification data of the calibration system includes the construction of multi-type fault and high-stability verification standard library data, and the construction of a verification mathematical model of a multi-objective optimization algorithm data, hybrid variable neighborhood algorithm for verification model solution data and calibration system optimal verification scheme data; the big data analysis of machine learning and the calculation of quality control charts include data mining, feature extraction and pattern recognition of online value traceability data and online verification data; the verification mathematical model data of the multi-objective optimization algorithm quantifies multiple indicators of electric energy metering instruments, and solves the optimal verification scheme through the optimization algorithm; the hybrid variable neighborhood algorithm solves the verification model solution data through a variety of variable neighborhood search strategies to obtain the optimal verification scheme; the high-precision electric energy meter also includes voltage channel, resistance voltage division sampling, current channel, current transformer sampling, dual-channel fully differential synchronous ADC converter, DSP data processing module, frequency division, low pulse and high pulse devices.

[0034] The principle block diagram of a standard electric energy meter is as follows: Figure 2 As shown. From the principle block diagram of the standard electric energy meter, it can be seen that the standard electric energy meter is composed of voltage and current acquisition channels, dual-channel fully differential synchronous ADC converter, DSP data processing module, etc. The basic design principle of the standard electric energy meter is: the sampling signals on the voltage and current channels are converted into small signals by resistor voltage division sampling and current transformer sampling respectively, and then A / D conversion is performed and sent to DSP for processing. DSP performs real-time U and I operations to calculate the actual power value P. P controls the frequency generator to generate a high-frequency pulse output proportional to P. This pulse is a high-frequency pulse of electric energy, which is divided by the frequency divider to obtain a low-frequency pulse of electric energy. The high-frequency and low-frequency electric energy pulses are output to the interface. While measuring P, the DSP also completes the measurement of the voltage and current true effective value phase and frequency.

[0035] Since the traditional standard energy meter is relatively large, taking the single-phase MDP1005A and three-phase MDP2005A as examples, the dimensions are about 450mm×445mm×145mm, which does not meet the size requirements for circulation on the automated verification system. Therefore, the traditional standard energy meter cannot be verified online in the automated verification system. The energy meter samples tested on the automated verification system cannot be used as verification standards because their accuracy level does not meet the requirements. This project intends to optimize and upgrade the current transformer, sampling resistor, sampling circuit, and reference voltage source in the energy meter based on the energy meter sample, improve the accuracy level of the energy meter, and develop an energy meter transfer standard that meets the accuracy level and size requirements and can be circulated on the automated verification system.

[0036] The four aspects that affect the accuracy of the verification standards of single-phase and three-phase energy meters are the built-in current transformer, sampling resistor, sampling circuit, and reference voltage source. Since the main difference between a three-phase energy meter and a single-phase energy meter lies in the difference in the internal circuit, a three-phase energy meter is composed of three single-phase energy meter circuits. Therefore, the following analysis is mainly based on the optimization of the internal structure of a single-phase energy meter.

[0037] The error of current transformer is mainly caused by the magnetomotive force. Previous analysis has discussed the influence of the structure and design of current transformer on the error. The following will analyze the influence of frequency on the current transformer error measurement system from three aspects.

[0038] According to the law of electromagnetic induction, the instantaneous induced electromotive force on the secondary side of the current transformer is:

[0039]

[0040] Assuming that the main magnetic flux changes according to the sinusoidal law, then:

[0041]

[0042] therefore:

[0043]

[0044] E m2 =N2ωφ m (1-4)

[0045] is the maximum value of the induced electromotive force, so the effective value of the secondary induced electromotive force is:

[0046]

[0047] According to the error formula of current transformer:

[0048]

[0049] Among them, B takes a valid value:

[0050]

[0051] According to the law of electromagnetic induction:

[0052]

[0053] It can be seen from formula (1-8) that the power supply frequency, average magnetic path length, magnetic permeability, and core cross-sectional area have a certain influence on the error of the current transformer. For the current transformer, the error will become larger as the power supply frequency decreases. If the frequency increases, the magnetic density in the core will decrease, and therefore the excitation current will also decrease. The reduction in excitation ampere-turns will inevitably reduce the ratio difference and phase angle difference of the current transformer, thereby improving the accuracy of the current transformer.

[0054] It can also be quantified and analyzed from the equivalent model of the current transformer: if the excitation and loss characteristics of the current transformer core and the parameters of the secondary winding are known, the error of the current transformer can be calculated more accurately through the formula.

[0055] The current transformer equivalent circuit model is as follows: Figure 3 As shown in the figure (converted to the secondary side), the primary current i1 is equal to the sum of the excitation branch current im and the load branch current i2. The excitation current im is the error source of the current transformer. The excitation current is the equivalent current that provides the core excitation flux and loss. It has a nonlinear function characteristic relationship with the primary side current. The excitation current distortion will be more obvious under the complex operation state of the high-voltage field.

[0056] Figure 3 The symbols in the table have the following meanings: i1: primary current converted to the secondary side; i2: secondary current; I0: current used for core excitation; R1: internal resistance of primary winding converted to the secondary side; L1: leakage inductance of primary winding converted to the secondary side; R2: internal resistance of secondary winding; L2: leakage inductance of secondary winding; Rm: equivalent resistance of core excitation loss; Lm: equivalent inductance of core excitation; ZL: load impedance of transformer.

[0058]

[0059]

[0060] For simplicity, assume that the load is purely resistive, represented by RL, and the ratio difference and phase difference of the current transformer can be deduced, as shown in formulas (1-9) and (1-10). The frequency will directly affect the ratio difference and phase difference of the current transformer. When the frequency increases, the ratio difference will decrease, and the phase difference will also decrease. The above analysis can only be applied to the case where the frequency changes little, but for wideband and ultra-high frequency, the error characteristics of the transformer need to be re-analyzed and discussed.

[0061] Embodiment 2, an online metering monitoring device of an automated calibration system for electric energy metering instruments, comprising an online value traceability device of the calibration system and an online verification device of the calibration system, wherein the online value traceability device of the calibration system comprises a high-precision electric energy meter, a precision voltage and current sampling device, a temperature-self-compensated voltage-based quasi-circuit, a high-precision mutual inductor, magnet material properties, cross-compensation shielding, and a storage device for standards pre-set by the calibration system; the online verification device of the calibration system comprises a storage device for constructing a multi-type fault and high-stability verification standard library, a verification mathematical model module for constructing a multi-objective optimization algorithm, a hybrid variable neighborhood algorithm for solving the verification model module, and a calibration system optimal verification solution module.

[0062] Embodiment 3, the computer device in the embodiment of the present invention may be an automatic calibration system for electric energy metering instruments, the system comprising a processor and a memory. The memory is used to store an online metering monitoring computer program for the automatic calibration system for electric energy metering instruments, the program comprising program instructions. The processor may be the central processing unit of the system, used to execute the online metering monitoring program instructions of the automatic calibration system for electric energy metering instruments stored in the memory. Through this embodiment, the system can automatically perform online metering monitoring of electric energy metering instruments to ensure its accuracy and reliability. The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices or transistor logic devices. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to execute the steps of an online metering monitoring method of an automatic calibration system for electric energy metering instruments.

[0063] Embodiment 4, the storage medium in the embodiment of the present invention may be a solid state hard disk, which is used to store an online metering monitoring computer program of an automatic calibration system for electric energy metering instruments, and the program includes program instructions. The solid state hard disk provides storage space, in which the operating system of the terminal and the program code for online metering monitoring are stored. The processor can load and execute the instructions stored in the solid state hard disk to implement the corresponding steps of the online metering monitoring method of the electric energy metering instrument. Through this embodiment, the automatic calibration system for electric energy metering instruments can realize accurate online metering monitoring, and improve the reliability and accuracy of the system.

Claims

1. An online measurement monitoring method for an automatic verification system of an electric energy metering instrument, characterized in that: The verification system provides the online value traceability data to the automated verification system, and the verification system provides the online verification data to the automated verification system. The automated verification system performs online value traceability and verification based on the above data, the big data analysis results of machine learning, and the calculation of the quality control procedure, and finally performs real-time online monitoring. The online value traceability data of the verification system includes high-precision electric energy meter data, precision voltage and current sampling device data, temperature self-compensated voltage basic quasi-circuit data, high-precision mutual inductor data, magnet material performance data, cross-compensation shielding data, and standard data preset by the verification system; The online verification data of the calibration system includes data for constructing a standard library for multi-type faults and high stability verification, data for constructing a verification mathematical model for a multi-objective optimization algorithm, data for solving the verification model using a hybrid variable neighborhood algorithm, and data for the optimal verification solution for the calibration system.

2. The method according to claim 1, characterized in that The big data analysis of machine learning and the calculation of quality control charts include data mining, feature extraction and pattern recognition of online measurement traceability data and online verification data.

3. The method according to claim 1 or claim 2, characterized in that: The verification mathematical model data of the multi-objective optimization algorithm quantifies multiple indicators of the electric energy metering device, and solves the optimal verification solution through the optimization algorithm.

4. The method according to claim 1 or claim 2, characterized in that: The hybrid variable neighborhood algorithm solves the verification model through a variety of variable neighborhood search strategies to obtain the optimal verification solution.

5. The method according to claim 1 or claim 2, characterized in that: The high-precision electric energy meter also includes a voltage channel, a resistor voltage division sampling, a current channel, a current transformer sampling, a dual-channel fully differential synchronous ADC converter, a DSP data processing module, a frequency division, a low pulse and a high pulse device.

6. The method according to claim 1, characterized in that The online value traceability device of the verification system includes a high-precision electric energy meter, a precision voltage and current sampling device, a temperature self-compensating voltage basic circuit, a high-precision mutual inductor, magnet material properties, cross-compensation shielding, and a storage device for pre-set standards of the verification system.

7. The method according to claim 1, characterized in that The online verification device of the verification system includes a storage device for building a multi-type fault and high-stability verification standard library, a verification mathematical model module for building a multi-objective optimization algorithm, a hybrid variable neighborhood algorithm for solving the verification model, and a verification system optimal verification solution module.

8. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, an online metering monitoring method for an automatic calibration system for electric energy metering instruments as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, an online metering monitoring method for an automatic calibration system of an electric energy metering device as described in any one of claims 1 to 7 is implemented.

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