Smart Metering Applications Research Articles

Research on modeling method for digitizing distribution substation areas based on big data from smart meters

With the wide application of smart meters, real-time data collection in power grid operation can be carried out in real time, which provides big data support for the digital modeling of distribution station areas. At the same time, the digital transformation of the distribution area can be promoted through the digital modeling of the distribution area. In this situation, how to use the big data of smart meters to achieve the digital modeling of the distribution area needs to be further studied. Firstly, this paper briefly introduces the connotation of digitalization in the distribution station area. At the same time, aiming at the digitalization of the distribution station area, it analyzes the digitalization of station features, user features, new energy features, network parameters and operation features, and obtains the digitalization model. Finally, on the basis of the above, select an application scenario to introduce the application of digitalization modeling. The research results can provide a reference for the combination of big data application of smart meters and digitalization of the distribution area, and help the digital transformation of the distribution area.

Research on the identification and detection technology of electricity theft in power distribution areas under a smart grid environment

Abstract With the extensive application of smart meters, power companies can access a wealth of customer electricity usage data, which has not yet been fully utilized. This paper proposes a substation area user identification and electricity theft detection method based on smart meter data, aiming to enhance the level of intelligent management of power grid enterprises and reduce operational costs. By using the grey relational analysis method, analyzing the measurement data collected by smart meters at different time points, it is possible to determine the substation area and phase to which the user belongs and to detect electricity theft behavior. Finally, through simulation, the effectiveness of the proposed method is verified.

Comparison of SQL, NoSQL and TSDB database systems for smart buildings and smart metering applications

Comparison of SQL, NoSQL and TSDB database systems for smart buildings and smart metering applications

Machine intelligence aware electricity theft detection for smart metering applications

Electricity theft detection (ETD) is a serious issue and needs proper attention to investigate and detect the fraudulent consumers. Fraudulent consumers steal electricity and burdensome utility providers, which causes huge revenue losses. To minimize such revenue loss an affine investigation and detection scheme is required. In this paper, we use AttenLSTMInception in order to investigate the electricity theft scenario. Initially, benign consumers data is manipulated through false data injection techniques (FDIs). Six various types of FDIs are used to manipulate the benign class data, which synthesizes six variants of the manipulated data. The benign and synthesized variants are concatenated and the data is fed to borderline SMOTESVM. In order to tackle the issue of synthesizing data from overlapped samples, borderline SMOTESVM is used. The balanced data is segmented into training and testing data. Furthermore, Matthews correlation coefficient is used to investigate model's performance by measuring confidence score of the binary classification. Moreover, to evaluate the robustness and to validate the performance and of the model, testing data of 20%, 40% and unseen is used. Similarly, six activation functions are used to evaluate the model's robustness. Furthermore, impact of the imbalanced data is studied using MCC score. Support vector machine (SVM), random forest (RF), decision tree (DT), convolutional neural network-based long short-term memorization (CNN-LSTM) are used as base models. Our model outperforms the base models by achieving high recall, precision, accuracy, AUC-score and F1 score. These values for our proposed model are 0.94%, 0.94%, 0.98%, 0.98%, respectively.

Electricity theft detection based on hybrid random forest and weighted support vector data description

Improving the detection rate of electricity theft users in smart grids is crucial to the safe operation of the power system and the economic efficiency of the grid. The traditional electricity theft detection relies on manual household inspection, which is costly and inefficient. With the application of smart meters, machine learning-based methods are gradually applied to detect electricity theft users. However, the training of the model is limited by the unbalanced electricity consumption dataset, and the performance of the model is limited in the process of processing high-dimensional data. Therefore, this paper adopts ensemble learning and imbalance data classification model to analyze the electricity theft detection problem in complex grid environment, and proposes an electricity theft detection model based on random forest and weighted support vector description. Firstly, the feature selection algorithm for mixed load data with statistical features based on random forest (FS-MLDRF) is used to extract the features that can best represent the customers’ electricity consumption behavior, so as to reduce the complexity of user data. Finally, an electricity theft detection based on hybrid random forest and weighted support vector data description is constructed (HFR-WSVDD), and the SVDD classifier is trained with the features weighted by feature importance to improve the performance of power theft detection in the case of unbalanced data. The simulation experiment results on the real electricity dataset show that compared with existing feature selection algorithms and anomaly detection algorithms, the algorithms proposed in this paper have excellent feature representation capability and better classification performance.

Intrusion detection in smart meters data using machine learning algorithms: A research report

The intrusion detection in network traffic for crucial smart metering applications based on radio sensor networks is becoming very important in the Smart Grid area. The network’s structure for smart meters under investigation should consider important security factors. The potential of both passive and active cyber-attacks affecting the functioning of advanced metering infrastructure is studied and a novel method is proposed in this article. The proposed method for anomaly identification is efficient and rapid. In the beginning, Cook’s distance was employed to recognize and eliminate outlier observations. After observations are made three statistical models Brown’s, Holt’s, and winter’s models were used for exponential smoothing and were estimated using the provided data. Bollinger Bands with the appropriate parameters were employed to estimate potential changes in the forecasts produced by the models that were put into operation. The estimated traffic model’s statistical relationships with its actual variations were then investigated to spot any unusual behaviour that would point to a cyber-attack effort. Additionally, a method for updating common models in the event of substantial fluctuations in real network traffic was suggested. The findings confirmed the effectiveness of the proposed method and the precision of the selection of the appropriate statistical model for the under-study time series. The outcomes validated the effectiveness of the proposed approach and the precision in choosing a suitable statistical model for the time series under investigation.

High spatial granularity residential heating load forecast based on Dendrite net model

With the application of smart meters, more information is available from residential buildings for support heat load forecast. Yet, there is still a lack of an effective method to exploit the value of the high spatial granularity information, particularly for residential communities with high randomness in human behaviors. To fill this gap, this paper proposes a data-driven heat load forecast method based on the field measurements of smart meters A white-box machine learning algorithm, namely Dendritic Network, is employed to aggregate and analyze data obtained from different locations of district heating systems. An online correction mechanism is then proposed to regulate the forecast horizon and ensure the adaptiveness of the machine learning model under different scenarios. The results demonstrate that the dendritic network employed in the proposed machine learning method shows higher forecast accuracy, the root-mean-square error is 0.0029, in some research cases the coefficient of determination can reach 0.99–1, and wide scope of application in the area of heat load forecast.

Smart Metering Roll-Out in Europe: Where Do We Stand? Cost Benefit Analyses in the Clean Energy Package and Research Trends in the Green Deal

2020 was a key year for several targets in European energy and climate policy, including the requirement for European countries to deploy smart metering for at least 80% of electricity consumers. This target was set to ease the transition towards a consumer-centered and digitalized energy system. In fact, there are numerous applications that are facilitated or are directly linked to smart meters. Among others: demand response programs that enable consumers to be active in the energy market, and remote grid monitoring by the Distribution System Operator. In this paper, we analyze the initial provisions of the Third Energy Package and those of the Clean Energy Package, with particular focus on the recently approved directive on common rules for the internal market for electricity and the newly introduced smart meters specifications. We present the highlights of the national cost–benefit analyses for smart metering roll-out, focusing on the decisions made by the Member States with respect to a potential smart meter roll-out, that was targeted to be completed by 2020 and present the current situation of smart metering roll-out. We also present and categorize some of the R&I smart grid projects realized over recent years, focusing on the ones that deal with smart metering integration in order to depict the smart metering applications and technologies tested on the ground. Therefore, this paper portrays a full picture with respect to smart meters in Europe today and gives insights for monitoring smart metering roll-outs taking into account the current trends in smart metering applications.

Implementation of Dynamic Controls for Grid-Tied-Inverters through Next-Generation Smart Meters and Its Application in Modernized Grid

In this paper, an introduction and comprehensive analysis have been presented for the implementation and application of modern smart meters which include Unbundled Smart Meters (USM) and Next-Generation Open Real-Time Smart Meters (NORM). This article also contributes to methods through which USM and NORM could provide a better perspective to the already available technologies for grid-tied-inverter controlled feeding renewables to the grid. The research proposes a next-generation smart meter model with the feature of a phasor measurement unit. The meter is further integrated with a controller board that controls the power injection from the inverter to the grid based on the real-time data obtained from the smart meter. The inverter is simulated with an open-circuit fault and is controlled to provide non-oscillatory power to the grid based on an instantaneous grid power factor or phase requirement. The proposed meter has the flexibility to add additional features to control the inverter based on other grid requirements such as active and reactive power control, tariff implementation, etc. This manuscript provides the analytical aspects of the use of smart meters in efficient energy management and also addresses the need for smart technologies for grid modernization.

Remote error estimation of smart meter based on clustering and adaptive gradient descent method

At present, the main way for electric power companies to check the accuracy of electric meters is that professionals regularly bring standard electric meters to the site for verification. With the widespread application of smart meters and the development of data processing technology, remote error estimation based on the operating data of smart meters becomes possible. In this paper, an error estimation method of smart meter based on clustering and adaptive gradient descent method is proposed. Firstly, the fuzzy c-means clustering method is used to preprocess the data to classify the operating conditions of each measurement, and then the adaptive gradient descent method is used to establish the error estimation model. The simulation results show that this method has high error estimation accuracy. This method has a small amount of calculation and high reliability and is suitable for large-scale power grids.

Efficient Energy Management for a Proposed Integrated Internet of Things-Electric Smart Meter (2IOT-ESM) System

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53 and the cases of the electrical energy theft if any below 20 and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected. In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53 and the cases of the electrical energy theft if any below 20 and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected.

Opportunities for thermoelectric generators in supporting a low carbon economy

Environmental pollution, global warming and increasing energy demands are urgent challenges facing society. Governments all over the world have set a national policy target for the transition to a zero or low carbon dioxide economy. As a result, scientists and engineers in industry and academia are working to develop cleaner, alternative and sustainable energy production technologies. One technology that has potential in this green technology transition is thermoelectric generators (TEGs), traditionally used off-grid and isolated from things such as stand-alone solar–thermal cells for military and aerospace applications such as missile-testing systems and space telescope cameras. However, future applications based on home entertainment, security systems and smart metering applications are imminent. Key limitations to this are low efficiency, high costs and self-heating with low thermal conductivity. Hence, this study aims to examine the current state of the art of TEGs and identify future research directions to achieve support for the green technology transition. The key findings of this study show that present successes will fulfill the future advancement of thermoelectric technology by supporting a low carbon dioxide economy.

An overview of DLMS/COSEM and g3-plc for smart metering applications

Abstract A crucial part of the smart grid communication network is the Advanced Metering Infrastructure (AMI), connecting a utility company to end-users to support telemetry and remote-control applications. Although different technologies and standards for smart metering systems exist, Power-Line Communication is the preferred technology for AMI networks in dense urban areas, and the G3-PLC, which uses DLMS/COSEM to support data exchange, is the most used standard in PLC-based AMI networks. This paper provides a holistic view of G3-PLC in smart metering systems, including its interaction with the DLMS/COSEM. It also covers the challenges and research opportunities in G3-PLC smart metering systems.

Aggregated Technical Commercial and Collection Loss Mitigation Through a Smart Metering Application Strategy

The privatization exercise that led to the creation of 11 distribution companies (Discos) is yet to yield the result of meeting the electricity supply needs of Nigeria. It is in realization of this fact that the Federal Government of Nigeria recently made different proposals and regulations to increase efficiency, availability, and competitiveness within the power industry. This article establishes a technical gain ratio to measure the impact of scaling up smart metering on the aggregated technical commercial and collection (ATC&C) losses. The ATC&C loss is the difference between the amount of electricity received by a distribution company from the transmission company and the amount of electricity for which it invoices its customers plus the adjusted collections loss. To achieve this objective, reviews of historical data of the Discos ATC&C losses and customers’ metering records as published by the Nigerian Electricity Regulatory Commission (NERC) for 2015, 2016, 2017, 2018, and 2020 were carried out. In addition, efforts were made to mathematically model the relationship between % metering and % ATC&C losses as this was the framework to help carry out effective forecasts and analyses of the study in order to show the impact level of the strategy employed. One of the salient technical contributions of this article was that it established that for every 1% increase in metering installation, there is a proportionate 0.8% decrease in ATC&C losses, provided all other factors responsible for technical and commercial losses remain constant. Consequently, improved ATC&C loss reduction would be achieved if Discos adopt a combination of other strategies that would ensure reduction in technical and commercial losses in addition to aggressive deployment of meter assets. However, in practice, factors causing technical and commercial losses are never constant as system components depreciate/burn out with time, energy theft, and pilferage, and meter tamper/bypass is on the increase daily; meter deployment is not aggressive enough to match utility customers’ growth. Hence, the adoption of combined modern strategies in addition to aggressive metering in tandem with customers’ growth has to be employed by Discos in a deliberate attempt to reduce ATC&C losses.

Characterization of DC series arc faults in PV systems based on current low frequency spectral analysis

This work presents an experimental study focused on the characterization of series arc faults in direct current (DC) photovoltaic (PV) systems. The aim of the study is to identify some relevant characteristics of arcing current, which can be obtained by means of low frequency spectral analysis of current signal. On field tests have been carried out on a real PV system, in accordance with some tests requirements of UL 1699B Standard for protection devices against PV DC arc faults. Arcing and non-arcing current signals are acquired and compared and the behavior of a set of indicators proposed by authors is analyzed. Different measurement equipment have been used, in order to study the impact of both measurement transducers and data acquisition systems on proposed indicators effectiveness. Presented results show that the considered indicators are suitable for detecting the arc presence even with commercial devices normally used for smart metering applications.

Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem.

The popularity of the Power Line Communication (PLC) system has decreased due to significant deficiencies in the technology itself, even though new wire installation is not required. In particular, regarding the request for high-speed throughput to fulfill smart-grid requirements, Broadband Power Line (BPLC) can be considered. This paper approaches PLC technology as an object of simulation experimentation in the Broadband Power Line Communication (BPLC) area. Several experimental measurements in a real environment are also given. This paper demonstrates these experimental simulation results as potential mechanisms for creating a complex simulation tool for various PLC technologies focusing on communication with end devices such as sensors and meters. The aim is to demonstrate the potential and limits of BPLC technology for implementation in Smart Grids or Smart Metering applications.

Communication Network Simulation for Smart Metering Applications: A Review

Many countries are witnessing the deployment of millions of smart meters, as they strive to upgrade their traditional grid to the smart grid already prominent in the developed parts of the world. Communication networks that can offer high-speed communication will be required for transmission of the copious amount of data resulting from the numerous smart meters and other intelligent electronic devices (IEDs) in the distribution network substation. Therefore, there is a need to critically assess the available communication infrastructure for eventual upgrade and deployment. In this paper, a survey of the communication network and networks simulation software used for this purpose was carried out, with specific emphasis on smart metering applications. Some critical requirements identified are security, quality of service (QoS) and optimal cost for system operation. Also discussed are methods employed in the works of literature to handle the critical issues raised. For data transmission between the meters and data concentrators; power line communication (PLC) was mostly cost-effective and hence, mostly deployed. In the wireless category, ZigBee (802.15.4), Wi-Fi (802.11), and WiMAX (802.16) among others are suitable and have been successfully deployed. OPNET, NS-3, NS-2, and OMNET++ have been successfully employed for communication network simulations in smart metering applications.

Correction to “Application of Smart Meters in High-Impedance Fault Detection on Distribution Systems” [May 19 3465-3473

Correction to “Application of Smart Meters in High-Impedance Fault Detection on Distribution Systems” [May 19 3465-3473

An Efficient IoT-based Smart Water Meter System of Smart City Environment

Water is a precious need of our lives. Due to the rapid population and urbanization, water usage monitoring is a significant problem facing our society. One solution is to control, analyze, and reduce the water consumption of the houses. The emerging of the Internet of Things (IoT) concept lately in our lives has offered the opportunity to establish water usage-efficient smart devices, systems and applications for buildings and cities. Many studies have suggested designing an IoT-based smart meter system; however, the IoT sensor node has limited studies, especially in battery life. Therefore, this study aims to implement and analyze an efficient data collection algorithm for IoT-based smart metering applications in consideration with energy consumption. The system items used are Arduino Uno, Wi-Fi-ESP8266, and water flow sensors. The applied algorithm is an efficient data collection algorithm for water meter (EDCDWM) to reduce the number of packet transmissions. Arduino performed this system’s implementation, while the simulation and analysis performed by MATLAB R2019b. The average percentage of energy saved by the applied algorithms of EDCDWM absolute change; and EDCDWM with relative differences in all nodes are around 60% and 93%, respectively.

Proposed Practical Communication Architecture for Automatic Fault Detection and Clearance in Secondary Distribution Power Network

A versatile yet secure communication architecture is important for smart grid (SG) applications. Most developing countries are currently deploying communication infrastructure for SG implementation. Survey in literature shows automated protection and control is already done in generation, transmission and primary distribution parts of the grid so as to guarantee reliable power supply to customers. However, secondary distribution power network (SDPN) is usually not monitored. Most of the published studies dealing with communication architectures in SDPN have focused on providing solutions for home automations and smart metering applications but little attention is paid to actual power utilities’ needs and implementation challenges facing power utilities specifically when detecting and clearing faults automatically in SDPN. Most power utility companies in developing countries and Tanzania in particular face a lot of challenges when dealing with faults management in the SDPN. This is due to poorly planned and unmonitored low voltage infrastructure. In this paper, practical requirements for SDPN communication architectures are established through challenge driven education (CDE) approach and thereafter, a practical communication architecture appropriate for Tanzania Electric Supply Company Limited (TANESCO) to enable automatic and real time faults detection and clearance in SDPN is proposed. Simulation results show that the developed architecture can successfully be used to practically detect and clear faults at SDPN since it introduces an overall end to end average delay of 1.362ms which is lower than what is specified by SG application standard delay time for distribution automation (100ms).