A Set of Methods for Predicting the Metrological Service-ability of Electricity Meters

In order to solve the problems of heavy workload, weak planning, and repetitive maintenance in the periodic rotation of smart energy meters, the author proposes a verification cycle optimization method based on the evaluation results of energy meter status. This method first obtains data on six indicators of smart energy meters: regional factors, reliability, full event, abnormal metering events, battery overload, and clock battery undervoltage; Subsequently, on the one hand, the coefficient of variation assignment method is used to obtain the status score of each electricity meter, and on the other hand, these six indicator data are used as input data, and the K means clustering algorithm is used to classify and obtain the corresponding categories. Finally, the two algorithms are combined to obtain a new method for evaluating the status of smart energy meters, and the final evaluation result is output. The experimental results indicate that: The number of electricity meters scored below 80 points obtained by this method accounts for 22.08% of the total number of electricity meters, while electricity meters scored above 80 points account for 77.93% of the total number of electricity meters. This indicates that this method is in line with the actual situation and objective laws. Constructing a state evaluation model for electric energy meters, using historical data and on-site calibration data as state variables, analyzing the annual operational quality of electric energy meters, and providing reference basis for adjusting the calibration cycle of electric energy meters.

The analysis and record of data for traditional in-field calibration of electric energy meter relies on artificial work, which will easily lead to data error due to human factors and unable to realize standardization of calibration. The complete sets of mobile operation software for in-field calibration of electric energy meter is embedded the process of in-field standardized operation, which can standardize the in-field calibration of electric energy meter and realize automatic identification of wiring mode and calibration methods of electric energy meter. It establishes the multiple data intelligent diagnostic model based on information such as current cutoff、 lose of phase、 voltage-loss and severe three-phase unbalance. By analyzing and comparing with the user data of Power User Electric Energy Data Acquire System, the model can automatically identify and diagnose abnormal power consumption behavior like stealing electricity. It also establishes a model for distinguishing the abnormal operating state of electric energy meter, which can realize automatic distinction of abnormal operating state of electric energy meter. Introduction Electric energy meter is a metrology apparatus for commercial exchange between electric power enterprises and customers, and calibration of electric energy meter is the duty of electric power marketing management which is very important to electric safety and power management. For a long time, the calibration personnel took the calibration instrument to the scene of the electric meter calibration, recorded the calibration data in the notebook, analyzed and screened the calibration data artificially, and then went back to the office to put the calibration data into the computer. The in-field calibration and background jobs in the office was performed separately, which cannot realize standardization of calibration and high-efficient work and analyzing and screening the calibration data artificially can easily lead to the data error of calibration. The complete sets of mobile operation software for in-field calibration of electric energy meter is embedded the process of in-field standardized operation, which can standardize the in-field calibration of electric energy meter. It also uses a variety of encryption technology to access intranet system securely, realizing a seamless connection with the Marketing Information System. The software employs an intelligent recognition and matching technology of calibration methods and establishes database of calibration methods of electric power meter to realize automatic identification of wiring mode and calibration methods of electric energy meter. It establishes the multiple data intelligent diagnostic model based on information such as current cutoff、 lose of phase、 voltage-loss and severe three-phase unbalance. By analyzing and comparing with the user data of Power User Electric Energy Data Acquire System, the model can automatically identify and diagnose abnormal power consumption behavior like stealing electricity. It also establishes a model for distinguishing the abnormal operating state of electric energy meter, which can realize automatic distinction of abnormal operating state of electric energy meter. 3rd International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2015) © 2015. The authors Published by Atlantis Press 1184 Fig.1. system architecture diagram

Electric energy meters are the most important measurement devices in State Grid. The measurement accuracy of electricity meters is related to the interests of users and the reputation of the enterprise, and the measurement error of electric energy meters is affected by various environmental factors, such as temperature and humidity. However, quantization of effects of complex working conditions on performance stability and measurement accuracy of the electric energy meters is few. In this paper, the basic error data of electricity meters running on site for more than one year in high and cold regions of Heilongjiang Province and high-altitude regions of Tibet Province are collected. The metering errors of the batches of electricity meters in the two test bases under the conditions of power factor 0.8C, current 0.1Ib, Ib and Imax are compared and analyzed. Finally, the trend of the variation of the errors of electricity meters under the condition of power factor 0.8C is analyzed with the combination of temperature and humidity. The results show that the measurement error of batch meters in the Heilongjiang base in spring, summer, autumn and winter was greater than that in the Tibet test base, especially in winter.

A remote monitoring technique for gateway electricity meters is introduced that leverages conservation principles of electric energy, voltage, and current. It aims to tackle issues related to errors in electric energy meters and transformers that are challenging to distinguish, as well as the high costs associated with on-site testing. The ridge regression is used to solve the formulas for bus power and current conservation to obtain gateway metering points’ error and current loops’ error. The voltage loop error is determined through an averaging method, which is then used to calculate the error in electric energy meters. The voltage consistency method is used to synchronize the time bias between gateway electricity meters, ensuring accurate calculations. The analysis results indicate that the proposed method effectively monitors the operating status of gateway electric energy meters and offers greater accuracy compared to benchmarks. This method can timely discover suspected inaccurate electricity meters, aiding in their maintenance and efficient operation.

Electrical resistance meter is the key tool in physics laboratory such as electricity meter experiment or DC power band experiment. The aim of this research is to design and develop electrical resistance meter for using in physics laboratory and to find the efficiency of the electrical resistance meter that has been created. The sample are students of Department of Electromechanic Manufacturing Engineering, Bansomdejchaopraya Rajabhat University. The Arduino microcontroller board is used for controlling the electrical resistance meter and finding the electrical resistance meter values, we use the principle of voltage division for the calculation with 24 resistance values. It is explored that the electrical resistance meter can actually be usable with high accuracy, which divided into 3 parts: 1) the percentage difference and the display result of the electrical resistance meter does not exceed 2 percent when compared with the Fluke 115 multimeter, 2) the evaluation by experts found that suitability of the electrical resistant meter has the average score of 3.96 which is at high level and 3) the satisfaction evaluation from the students of the electrical resistance meter has the average score of 4.01 which was also at high level.

Based on the big data analysis method, the verification and verification system of electric metering pipeline meters is designed. This paper expounds the working principle of electric energy metering device and intelligent electric energy meter in the automatic verification system of electric power metering pipeline meters. Combined with the principle of big data analysis, it collects the operation data of electric metering pipeline meters. By optimizing the hardware structure of the system, such as mobile terminal and sensor, it can improve the safety and stability of the system operation, and in the structure of the Internet of things, the power metering flow. The design of knowledge base and rule base is carried out in the verification and verification system of water line electric meter, and the operation flow of system software is improved, so as to assist the verification and verification system of electric metering pipeline meters, so as to quickly complete the operation and maintenance processing, and achieve the research effect of improving the production capacity and qualification rate of electric metering pipeline meters. Finally, according to the requirements of 596-2012 verification regulation, the performance of the verification and verification system of electric metering pipeline meters is tested, and the error value of electric energy in the system is detected to judge the operation safety set stability of the verification and verification system of electric metering pipeline meters. The research proves that the system can effectively meet the requirements of automatic verification of electric energy meters in the practical application process, effectively improve the application level of electric energy measurement technology, and improve the management effect of electric power enterprises.

The communication protocol structure of digital substation in smart grid has changed greatly, and the electrical energy metering system is changed greatly too. In this paper, an all-digital electrical energy metering system based on pure optical voltage transducer (OVT) and optical current transducer (OCT) is introduced. The advanced communication protocol IEC618509-2 is adopted in the all-digital electrical metering system firstly. And in this paper, the difference between traditional electrical energy metering system and all-digital electrical energy metering system is analyzed and compared in principle, and the benefits of all-digital electrical energy metering system are described. The realization of the proposed all-digital electrical energy metering system on a 220 kV voltage grade power system is described. The results of running show that the developed all-digital electrical energy metering has the performances of high accuracy, high reliability and high safety.

Electricity meters are devices that record the electrical energy consumed by the customer. Recently, the electricity meter has developed as a result of technological progress, and it transformed from an electro-mechanical energy meter to become an electronic meter. The electronic energy meter consists of electronic components which may change its properties due to exposure to different climatic environments, so climatic tests must be conducted on the electricity meter [1]. Solar radiation testing is considered most important for an outdoor electrical energy meter, performed to ensure that the meter does not change its characteristics or that its insulation does not fail during exposure to solar radiation [2]. Electricity meters are classified according their installation for either indoor or outdoor energy measurements [3]. Outdoor electricity meters are mostly protective class two which means that the insulation level is higher than that used in indoor energy meters and can be more exposed to direct sunlight. Solar tests are carried out on electricity meters according to the international standard of IEC62052-11:2020, and IEC60068-2-5:2010 [3], [4] at an irradiance value equal to 1120 W/ m²± 10%.

During the operation period of smart electricity meters, there is great uncertainty for the health index of smart electricity meters in batches. In order to better evaluate the health state of electric energy meters, this paper proposes a method for health analysis of electric energy meters based on the KNN(K-Nearest Neighbor) algorithm and structural equation model. To be specific, the key indicators are selected and the evaluation system is constructed through the KNN algorithm firstly. Then, the influence relationship and weight value between each key index are analyzed and determined by utilizing the structural equation model. Finally, the health of the smart meter is analyzed and predicted based on the weighted average method. This method has realized the transformation from traditional manual diagnosis to intelligent analysis and judgment based on machine learning. The empirical analysis shows that the proposed method could reduce the influence of human subjective factors in the health evaluation model, distinguishing the health degree of different batches accurately, and provide a convenient and accurate evaluation method for the front-line staff.

In order to study the quality analysis method of key links in smart energy meters, the author proposes a data fusion based quality analysis and prediction method for smart energy meters. This method is based on the relevant data of key links in the electric energy meter, and selects the data of the electric energy meter in research and development design, material procurement, production and manufacturing, acceptance testing, installation and operation, dismantling and scrapping as the sample data for model construction. The XGBoost algorithm classification method is used to establish an intelligent electric energy meter quality analysis model. Taking the dismantled electricity meter data of a certain power company as an example, this paper conducts modeling analysis and prediction of various quality issues of smart electricity meters, and conducts on-site verification. Based on the verification results, the model is continuously optimized. The results indicate that: The model was optimized using cross validation and grid search methods, and the final model achieved an accuracy rate of 0.74 and a recall rate of 0.82 on the validation set. This method can meet the actual needs of power grid business and objectively reflect the quality situation of key links in smart energy meters.

Design the electric energy meter constant magnetic field verification device system, carry out the constant magnetic field verification, and realize the electric energy meter constant magnetic field automatic verification. To ensure the accuracy and traceability of the magnetic field, a magnetic field sensor is installed at the bottom of the tray, and the test magnetic field is read in real time through the magnetic field sensor. The device is fixedly connected with an infrared sensor to detect the rotation angle of the magnetic steel. The operation of the three-axis motor of the magnetic field detection device changes the position of the magnetic steel around the bottom and top of the electric energy meter to complete the verification of the entire electric meter. The electric energy meter constant magnetic field verification device has high accuracy, strong safety, and can complete the automatic detection function.

Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.

With the continuous changes of the working environment of the electric energy meter, the dynamic detection method of the electric energy meter becomes more and more important. Therefore, in recent years, the IEC 62052-11 Standards Committee has proposed to add a rapid change test of electric energy meter load current. This article proposes an effective dynamic test method according to the requirements of this regulation standard, builds an electric energy meter dynamic error test platform and develops a software test system. An anti-submarine mechanism is proposed for the electric energy measurement algorithm of the smart meter, and the load current rapid change test of various electric energy meters from different manufacturers at home and abroad is tested, and the correctness of this test method is verified.

According to the analysis of the requirements of the equipment application experiment, it can be seen that the equipment requirements and application methods of electromagnetic compatibility conduction anti-interference degree experiment are similar, so the electricity meter inspection device will be used as auxiliary equipment. In practice, the power supply and error processor of the device will be affected by the interference signal transmitted by the system, and the phenomenon of crash and digital jumping will occur in the process of experimental operation, which can not run normally during the working period. By using the filtering technology, the conduction interference in the system can be controlled comprehensively, which can ensure the orderly completion of the experiment and exploration work of the electricity meter inspection device. Therefore, in this paper, based on the understanding of electromagnetic compatibility and electricity meter application detection technology, according to the electromagnetic compatibility test method of electricity meter, the design of the corresponding experimental system, and finally the application performance of the system for practical verification analysis.

In recent years, with the State Grid Corporation's vigorous construction of smart grids, smart meters have been rapidly promoted in the power system. After several years of online operation, the operating reliability of smart meters has also stabilized. However, in the process of widespread use of smart electric energy meters, the quality problems of clock chips, batteries and other components have also been exposed. This article takes the component failures encountered in the detection and operation of the smart electric energy meter as an example, analyzes the causes of the failures based on the four typical abnormal phenomena, and then conducts the opening detection for the above reasons and determines the nature of the failure based on the test conclusions. Finally, a corresponding fault management platform is established through the collection of electric energy meter fault data, which realizes all-round management and control of the fault handling process and selection of the qualifications of smart meter suppliers, thereby ensuring the quality of components and improving the operation of smart electric meters reliability.