Electric Energy Meter Research Articles

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Research on automated verification method for temperature-related tests of electrical energy meters

Abstract Program of pattern evaluation of fixed electrical energy meters-alternating current and GB/T 17215 series of national standards for the examination of energy meter performance by the impact of temperature change, set up a variety of temperature-related test items. Systematic research is possible to conclude that temperature-related tests can be divided into two categories according to the test procedure: Hold at test temperature for a certain period of time, return to the reference temperature and then measure the relevant parameters; measurement of the relevant parameters immediately after a certain period of time at the test temperature. Relevant parameters include errors at different load points and daily timing error of the internal clock of the energy meter. Data processing is undertaken after the test mainly consists of deriving error shift and mean temperature coefficient. This automated calibration method allows you to freely set the test temperature, test duration, reference temperature, stabilization duration, detection parameters, test points and data processing according to the test requirements. The paper explains in detail the application of the automated calibration method to realize typical temperature dependence, test of time-keeping accuracy of the energy meter with temperature and climatic tests (high temperature test, low temperature test and durability test of accuracy) The method breaks the shackles of the traditional calibration method of fixed test temperature, fixed test points and fixed test duration. It can adapt to the development needs of different levels of pattern evaluation of energy meter and national standards, for the laboratory to realize automated testing to provide the basis for industry standards and product testing commissioned inspection.

Research on calibration method for measurement error of multiple rate carrier energy meter

Abstract With the rapid development of smart grid technology, the multi-rate carrier watt-hour meter is an important measurement tool in power systems; its accuracy directly affects economic benefits and energy management efficiency. This paper proposes a calibration method of measurement error for a multi-rate carrier watt-hour meter to improve the measurement accuracy and reliability of the watt-hour meter under changing load conditions. The multi-rate carrier energy data is collected in real-time by high-precision data acquisition equipment to ensure the comprehensiveness and accuracy of the data. Using advanced data analysis technology to extract the amplitude characteristics of abnormal metering data, on this basis, the output deviation of equipment without input signal is eliminated, and the metering error calibration of the electric energy meter is completed. The experimental results show that this method can significantly improve the metering accuracy of multi-rate carrier watt-hour meters under different power consumption conditions and has good adaptability and practical value.

Analysis of Influence of Wiring Error on Electric Energy Metering Device and Preventive Measures

Electric energy metering device is the key to ensure the economic interests of power supply enterprises and users and the safety of electricity. Wiring errors are the main causes of electric energy metering distortion and power line failure. Five types of wiring errors of electric energy metering devices and their possible effects are described and sorted out, and the corresponding detection methods for software and hardware wiring errors are introduced. Based on the actual application of the electric energy metering device, the corresponding preventive measures are put forward, in order to provide some reference for solving the problem of wiring errors in the metering system of the electric energy metering device and ensuring the safety of electricity use.

Research on Electric Energy Metering Anomaly Detection and Classification Algorithm Under Multi-Source Data Fusion

The renewable energy grid is a distributed power grid, which needs to be parallel to the thermal power grid to ensure the stability of its own power generation. Therefore, renewable energy has the problem of multiple sources of power generation data and complex electric energy metering, which has always limited the development of renewable energy. In order to improve the grid-connected power supply quality of renewable energy and improve the accuracy of electric energy metering, this paper proposes a binary classification algorithm to collect, amplify and standardize multi-source data. Then, the Fourier function is used to normalize the multi-source data to shorten the differences between different devices. In particular, the attribute features are classified and the input data adjustment parameters are set. Finally, renewable energy's electric energy data value is measured, and the abnormal data characteristics are obtained through the second-order derivative to realize data mining and electric energy metering anomaly detection. The simulation results show that the binary classification can complete the metering and anomaly detection of electric energy in the distributed power grid, and the classification processing can simplify the complexity of multi-source data, improve the accuracy of metering anomaly detection, and make its dispersion more reasonable, and the results are between 90~95%, which is better than the online monitoring method. The time for anomaly result detection is 25s, which is shorter than the previous algorithm, and cross-domain data search is realized, with a cross-domain rate of 50~60%. Therefore, the classification algorithm proposed in this paper can meet the requirements of electric energy metering under multi-source data fusion and achieve rapid anomaly detection.

Research on failure rate evaluation test method of energy meter in charging pile

Abstract To verify the failure rate of intelligent energy meter in charging pile and whether it can meet the reliability requirements in “general technical specification for single-phase intelligent electric energy meter (2020)”. Take 20 smart meter samples from four manufacturers, use the accelerated life model and carry out the accelerated test in the way of stress acceleration for a total of 4000 hours, and evaluate the failure rate indicators under normal working stress. The test results show that the single-phase intelligent watt hour meters produced by the four manufacturers can meet the reliability requirements such as annual failure rate index. The accelerated test theory is applied to the reliability life and failure rate evaluation of intelligent electric energy meter to shorten the test time, improve the test efficiency and reduce the test cost.

Extraction of amplitude-domain characteristics from dynamic electric signals that cause large errors in electricity metering

Abstract Recent studies have shown that the power and current of high-power dynamic loads exhibit dynamic behaviors that cause large errors in electrical energy meters. Aimed at fast variations and large fluctuations in dynamic electric signals, this study examines a characteristic extraction method for small-time granularity and the impact of these characteristics on electrical energy meters. First, we constructed quasi-steady and dynamic model parameters for the dual-modal modulation models of current and power signals. Second, based on the two sets of signals sampled from the electrified railway traction substation and steelmaking arc furnace substation, we extracted quasi-steady and dynamic signal model parameters from fundamental active power signals. Third, we proposed three novel characteristic parameters by mapping the model parameter method, and extracted the four important characteristics. Finally, we designed a set of on-off-keying test signal experiments to determine the impact of the proposed characteristic parameters on the electric energy meter. The experimental results show that the electrical energy meter errors are sensitive to the proposed characteristic parameters, wherein the maximum impulse coefficient is the most sensitive characteristic of the EEM, which causes an error as large as −81.28%. The proposed characteristic parameters can be used as the input characteristic information of dynamic test signals for type testing and updating the test standards for electrical energy meters.

Правоотношения по коммерческому учету электрической энергии при применении интеллектуальных систем учета

The legal model of the application of intelligent electric energy metering systems provided for by the current regulatory regulation imposes the obligation of commercial energy accounting on entities that are not parties to the energy supply contract, which determines the task of creating an effective legal mechanism for its execution. Evaluation of the effectiveness of the legal protection of the parties to the energy supply contract within the framework of the legal relationship on commercial accounting of electric energy with the use of intelligent electric energy accounting systems; identification of problems arising in the implementation of such protection and justification of proposals to overcome them. The necessity of regulatory fixing of the penalty ensuring the fulfillment of the obligation tric energy with the use of intelligent accounting systems by guaranteeing suppliers and grid organizations in favor of the parties to the energy supply contract is substantiated.

On-line error monitoring of capacitive voltage transformer based on substation topology and distribution analysis

Abstract The capacitor voltage transformer (CVT) is widely used in power systems with high voltage levels. The operation state of CVT is related to the accuracy of electric energy metering. Because the internal structure of CVT is complex and vulnerable to the external environment, the error may be out of tolerance in the actual operation process. The error is composed of system error and random error. The change of error is mainly the change of system error. In general, random errors satisfy specific distributions and will not change easily. In this paper, the voltage amplitude and phase effective value output by the CVT acquisition device at the same voltage level are used to compare the amplitude effective value of the in-phase output to obtain the ratio characteristic quantity K, and the phase effective value is subtracted to obtain the difference characteristic quantity θK . K and θK contain the information of two CVT ratio differences and angle differences. The probability density distribution obeyed by K and θK during the normal operation of CVT is taken as the reference probability density distribution and compared with the probability density distribution to be measured during online monitoring. If the probability density distribution changes significantly, it shows that the ratio difference and angle difference have exceeded the difference under the premise of this paper. Finally, the contribution method is used to locate the out-of-tolerance CVT. In this paper, the online monitoring method is verified by simulation data. The test results show that the ±0.1% range ratio difference and the ±5′ range angle difference can be identified, and the online monitoring of the 0.1-stage CVT error is realized.

High-precision substation electric energy metering device based on an adaptive algorithm

Abstract With the rapid development of science and technology, the structure of new power systems continues to advance, and the requirements for the digitalization and intelligence of power network operation equipment continue to improve. The use of electricity is increasing year by year, and the importance of using a variety of technologies to improve the accuracy of energy metering devices is becoming increasingly prominent. In this paper, a design scheme of a high-precision energy metering device based on an adaptive algorithm is proposed based on the in-depth study of many kinds of current energy metering terminals, in combination with the current detection technology and embedded technology. By improving the accuracy of the electric meter, the accurate measurement of electric energy can be achieved, and the operation ability of the power grid can be improved.

Measurement analysis of three phase intelligent electricity meter based on nonlinear load

In order to improve the accuracy of non-linear load measurement in three-phase smart meters, the author proposes a measurement analysis of three-phase smart meters based on non-linear loads. The author analyzed the nonlinearity of most differential amplifiers in metering chips, theoretically analyzed the reasons for low metering accuracy under non-linear loads, and analyzed and designed the reasons for phase errors in electric energy meters. The results indicate that: At 5 %, 20 %, 100 %, and 120 % of the rated current, the phase error limits of the current transformer are ±15 ', ±8', ±5 ', and ±5', respectively. At 5 % of the rated current, the phase error generated by the transformer reaches 100 % of the rated current and 3 times that of 120 %. This is also the reason for the large error in measuring small current signals in electric energy meters. ConclusionThe author provides a precise analysis and design method for nonlinear load measurement of three-phase smart meters to improve the performance of measurement chips and improve the accuracy of nonlinear load measurement.

Monitoring System for Electrical Energy Use and Charging Electricity Tokens Based on Website and Whatsapp Application

The large number of equipment or electrical loads used causes large amounts of electrical energy usage. To determine the amount of electrical energy used, you can install an electrical energy meter whose results can be monitored via an LCD display. In this research, a website-based application was designed that can monitor electrical energy usage, by providing information in the form of voltage, current, power, and active power usage over time. Apart from that, this website can also top up electricity tokens as a feature to limit the use of electrical energy that will be used by users and is equipped with a notification message for remaining electricity tokens to the Whatsapp application. This system was developed using an ESP32 microcontroller which will provide data, process data, and present data from instrument measurements, then send the measurement results data every 15 seconds to the monitoring website. From the test results, the tool can measure electrical quantities such as current and voltage. The tool can also send measurement results to the website and store measurement data in a database. Test results for sending notification messages, the message was successfully sent to WhatsApp media if the electricity token met the specified conditions.

Application of Clustering Algorithm in Fault State Classification of Low Voltage Watt-Hour Meter

In this paper, a low-voltage digital watt-hour meter monitoring system for smart grid is designed. The system includes information acquisition system, low-voltage digital meter and power service system. The low-voltage digital meter uses RFID chip inside, which can accurately locate the target through high-speed transmission and remote identification. It can effectively overcome the security risks caused by information collision and the problem of slow recognition speed. According to different fault categories, community clustering method is used to classify them. In the actual determination of the fault causes of 32 electric energy meters with this method correctly analyzed the fault causes of 26 electric energy meters. The accuracy rate reached 81.25%.

Electric Energy Metering Error Evaluation Method Based on Deep Learning

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified.

A Method for Analyzing the Operating Data of Electric Energy Meters Based on Data Mining Analysis

Aiming at the problem of error estimation of smart meters in distribution network, a method of error estimation of smart meters based on particle swarm optimization convolutional neural network is proposed. This method establishes an intelligent energy meter error estimation model through data collection, data prediction, and preprocessing. To address the convergence issue in training, the interlayer distribution of weights is adjusted to improve training quality. This method fully utilizes template calibration information to transform indicator detection under complex conditions into simple and effective isometric segmentation, transforming label recognition from complex text detection and recognition tasks to simple and efficient binary detection tasks, with better robustness. The effectiveness and high robustness of the proposed method have been demonstrated through experimental verification.

AUTOMATED SYSTEM FOR DETERMINING OF ELECTRICITY CONSUMPTION WITH ELEMENTS OF ARTIFICIAL INTELLIGENCE

Currently, Russia is moving towards automating the collection of information from metering devices for heat, gas, water, electricity, etc. Technologies are being developed that allow the transmission of resource consumption data via various communication channels, both wired and over-the-air. There are already devices and devices that allow transmitting readings of electricity metering devices over existing wired communication networks and this issue is being resolved at the legislative level, Federal Law No. 522-FZ of 07/01/2020 has been adopted. The basis for the research is to save time and effort in obtaining the necessary information to account for electricity readings from substations, increase the speed of decision-making, and be able to predict the situation with big data to identify abnormal values and eliminate them (for example, identified data from electricity meters will reduce the time labor of metering operators and economic losses of an energy supply company). The article proposes hardware and software methods for detecting data on electricity losses based on the use of artificial neural network algorithms and allowing to detect inconsistencies in commercial data readings of electric meters, which will reduce the commercial component of electricity losses. A verification calculation of an artificial neural network was performed for inconsistencies in the data on the transmitted electricity. The design of a receiving and transmitting digital module for remote data recording has been carried out, as well as the development of an automated system designed for remote collection, processing, storage and transmission of information on energy consumption and balance. The developed automated information and measurement system for remote data collection and transmission was tested at the manufacturer of smart electric energy meters with a radio channel.

Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high-voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high-voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high-voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high-voltage electric energy metering transformers, and the fault diagnosis effect is outstanding.

Analysis of factors influencing electric energy measurement in power systems

This article focuses on summarizing and analyzing the causes of errors in electric energy meters under harmonic conditions, clarifying the relationship between errors and frequency characteristic curves, and pointing out that the positive and negative errors mainly depend on the direction of each harmonic power relative to the fundamental power, the properties of the load, the differences in physical properties of electric energy meter manufacturing and material formation, etc.

Scheduling optimization of electric energy meter distribution vehicles for intelligent batch rotation

As industrial technology continues to advance through integration, society's demand for electricity is rapidly increasing. To meet the requirements of refined grid management and address the elevated challenges arising from the increased electrical load, this paper delves into the investigation of distribution vehicle scheduling for the practical scenario of batch rotation of smart meters. Initially, based on the practical distribution task requirements of a provincial metrology verification center, a multi-level optimization model is constructed for the batch rotation and distribution vehicle scheduling of smart meters. The primary objective is to maximize the enhancement of smart meter distribution efficiency while minimizing the overall distribution cost. Moreover, this paper introduces a refined Grey Wolf Optimization algorithm (OLC-GWO) based on Opposition-based Learning, Levy flight strategy, and Cauchy mutation to solve the model. By generating an opposite population to improve the quality of initial feasible solutions and further harnessing the global search capabilities of Levy flight and Cauchy mutation operators, the algorithm's effectiveness is enhanced. The algorithm is subjected to testing using multiple benchmark functions and its performance is compared with variants of GWO, as well as several cutting-edge intelligent optimization algorithms including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Honey Bee Algorithm (HBA). The results indicate that OLC-GWO exhibits excellent performance in terms of convergence speed and optimization capability. Finally, the improved algorithm is subjected to simulation experiments by incorporating order data from the practical distribution operations of a provincial metrology verification center. The outcomes verify the efficiency of the proposed algorithm, reinforcing the practical significance of the established model in addressing the real-world challenge of batch rotation and distribution vehicle scheduling for smart meters.

Degradation Analysis: Modeling and Evaluating for Electric Energy Meters under Multi-stress

The measurement accuracy of electric energy meters (EEMs) is crucial for respecting the fairness of the electricity market and the justice of electric energy settlement. However, the basic error (BE) of the measurement suffers from external stress, especially under extreme environments. It is challenging to evaluate the degradation trend of EEMs under multi-stress. To address this issue, this paper proposes modeling and evaluating methods for precise degradation analysis of EEMs under multi-stress. First, an optimized k-nearest neighbor (OKNN) is proposed to correct the outliers, where the stress-related weight and weighting factor are designed. Then, a varied-bias wiener process with hierarchical Bayesian (VWHB) model is introduced to evaluate and explore the impact of stress on the BE. The multi-stress, as well as the BE, are fused to parameterize this impact. Integrating the OKNN and VWHB, a degradation analysis framework is further proposed to model the data and conduct the degradation analysis. Extensive experiments on field data collected from the typical operating environment lab demonstrated that the proposed degradation analysis framework reveals superior performance than some state-of-art approaches. The root of the mean of the square of errors and the mean of absolute value of errors of OKNN-VWHB are the lowest with 0.0356 and 0.0281, respectively, for all the EEMs from three different companies.