scholarly journals Communication Performance Assessment for Advanced Metering Infrastructure

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 88 ◽  
Author(s):  
Jen-Hao Teng ◽  
Chia-Wei Chao ◽  
Bin-Han Liu ◽  
Wei-Hao Huang ◽  
Jih-Ching Chiu
Keyword(s):  
Smart Grids ◽  
Small Scale ◽  
Smart Meter ◽  
Advanced Metering Infrastructure ◽  
Smart Meters ◽  
Communication Performance ◽  
Reading Success ◽  
Efficient Communication ◽  
Advanced Metering ◽  
Communication Paths

Advanced Metering Infrastructure (AMI), the foundation of smart grids, can be used to provide numerous intelligent power applications and services based on the data acquired from AMI. Effective and efficient communication performance between widely-spread smart meters and Data Concentrator Units (DCUs) is one of the most important issues for the successful deployment and operation of AMI and needs to be further investigated. This paper proposes an effective Communication Performance Index (CPI) to assess and supervise the communication performance of each smart meter. Some communication quality measurements that can be easily acquired from a smart meter such as reading success rate and response time are used to design the proposed CPI. Fuzzy logic is adopted to combine these measurements to calculate the proposed CPI. The CPIs for communication paths, DCUs and whole AMI can then be derived from meter CPIs. Simulation and experimental results for small-scale AMIs demonstrate the validity of the proposed CPI. Through the calculated CPIs, the communication performance and stability for AMI can be effectively assessed and supervised.

scholarly journals Data falsification attacks in advanced metering infrastructure

2021 ◽  
Vol 10 (1) ◽  
pp. 412-418
Author(s):  
Hasventhran Baskaran ◽  
Abbas M. Al-Ghaili ◽  
Zul- Azri Ibrahim ◽  
Fiza Abdul Rahim ◽  
Saravanan Muthaiyah ◽  
...  
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Smart Grids ◽  
Supply And Demand ◽  
Effective Control ◽  
Smart Meter ◽  
Advanced Metering Infrastructure ◽  
End User ◽  
Smart Meters ◽  
Advanced Metering

Smart grids are the cutting-edge electric power systems that make use of the latest digital communication technologies to supply end-user electricity, but with more effective control and can completely fill end user supply and demand. Advanced Metering Infrastructure (AMI), the backbone of smart grids, can be used to provide a range of power applications and services based on AMI data. The increased deployment of smart meters and AMI have attracted attackers to exploit smart grid vulnerabilities and try to take advantage of the AMI and smart meter’s weakness. One of the possible major attacks in the AMI environment is False Data Injection Attack (FDIA). FDIA will try to manipulate the user’s electric consumption by falsified the data supplied by the smart meter value in a smart grid system using additive and deductive attack methods to cause loss to both customers and utility providers. This paper will explore two possible attacks, the additive and deductive data falsification attack and illustrate the taxonomy of attack behaviors that results in additive and deductive attacks. This paper contributes to real smart meter datasets in order to come up with a financial impact to both energy provider and end-user.

scholarly journals An enhanced broadband class-J mode power amplifier for 5G smart meter applications

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1099
Author(s):  
Nagisetty Sridhar ◽  
Dr Chinnaiyan Senthilpari ◽  
Dr Mardeni R ◽  
Dr Wong Hin Yong
Keyword(s):  
Power Amplifier ◽  
Smart Grids ◽  
High Efficiency ◽  
Fine Tuning ◽  
Smart Meter ◽  
Smart Meters ◽  
Power Added Efficiency ◽  
Matching Networks ◽  
Grid Applications ◽  
Advanced Metering

Background: With the tremendous increase in the usage of smart meters for industrial/ household purposes, their implementation is considered a crucial challenge in the Internet of Things (IoT) world, leading to a demand for emerging 5G technology. In addition, a large amount of data has to be communicated by smart meters efficiently, which needs a significant enhancement in bandwidth. The power amplifier (PA) plays a major role in deciding the efficiency and bandwidth of the entire communication system. Among the various modes of PAs, a newly developed Class-J mode PA has been proven to achieve high efficiency over a wide bandwidth by maintaining linearity. Methods: This paper proposes a Class-J mode PA design methodology using a CGH40010F-GaN device that operates at a 3.5 GHz frequency to meet the requirements of 5G wireless communication technology for the replacement of existing 4G/LTE technology used for advanced metering infrastructure (AMI) in smart grids. This research's main objective is to design the proper matching networks (M.Ns) to achieve Class-J mode operation that satisfies the bandwidth requirements of 5G smart grid applications. With the target impedances obtained using the load-pull simulation, lumped element matching networks are analyzed and designed in 3 ways using the ADS EDA tool. Results: The simulation results reveal that the proposed Class-J PA provides a maximum drain efficiency (D.E) of 82%, power added efficiency (PAE) of 67% with 13 dB small-signal gain at 3.5 GHz, and output power of 40 dBm (41.4 dBm peak) with a power gain of approximately 7 dB over a bandwidth of approximately 400 MHz with a 28 V power supply into a 50 Ω load. Conclusion: The efficiency and bandwidth of the proposed Class-J PA can be enhanced further by fine-tuning the matching network design to make it more suitable for 5G smart meter/grid applications.

scholarly journals Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 653 ◽  
Author(s):  
Xiaoyao Huang ◽  
Tianbin Hu ◽  
Chengjin Ye ◽  
Guanhua Xu ◽  
Xiaojian Wang ◽  
...  
Keyword(s):  
Data Storage ◽  
Classification Accuracy ◽  
Smart Grids ◽  
Fine Tuning ◽  
Smart Meter ◽  
Smart Meters ◽  
Softmax Classifier ◽  
Data Mining Approach ◽  
Data Volume ◽  
Advanced Metering

With the development of advanced metering infrastructure (AMI), electrical data are collected frequently by smart meters. Consequently, the load data volume and length increase dramatically, which aggravates the data storage and transmission burdens in smart grids. On the other hand, for event detection or market-based demand response applications, load service entities (LSEs) want smart meter readings to be classified in specific and meaningful types. Considering these challenges, a stacked auto-encoder (SAE)-based load data mining approach is proposed. First, an innovative framework for smart meter data flow is established. On the user side, the SAEs are utilized to compress load data in a distributed way. Then, centralized classification is adopted at remote data center by softmax classifier. Through the layer-wise feature extracting of SAE, the sparse and lengthy raw data are expressed in compact forms and then classified based on features. A global fine-tuning strategy based on a well-defined labeled subset is embedded to improve the extracted features and the classification accuracy. Case studies in China and Ireland demonstrate that the proposed method is more capable to achieve the minimum of error and satisfactory compression ratios (CR) than benchmark compressors. It also significantly improves the classification accuracy on both appliance and house level datasets.

scholarly journals Smart Meter Traffic in a Real LV Distribution Network

Energies ◽  
2018 ◽  
Vol 11 (5) ◽  
pp. 1156 ◽  
Author(s):  
Nikoleta Andreadou ◽  
Evangelos Kotsakis ◽  
Marcelo Masera
Keyword(s):  
Distribution System ◽  
Low Voltage ◽  
Distribution Network ◽  
Distribution Networks ◽  
Smart Meter ◽  
Advanced Metering Infrastructure ◽  
Distribution Grid ◽  
Message Size ◽  
Real Distribution ◽  
Advanced Metering

The modernization of the distribution grid requires a huge amount of data to be transmitted and handled by the network. The deployment of Advanced Metering Infrastructure systems results in an increased traffic generated by smart meters. In this work, we examine the smart meter traffic that needs to be accommodated by a real distribution system. Parameters such as the message size and the message transmission frequency are examined and their effect on traffic is showed. Limitations of the system are presented, such as the buffer capacity needs and the maximum message size that can be communicated. For this scope, we have used the parameters of a real distribution network, based on a survey at which the European Distribution System Operators (DSOs) have participated. For the smart meter traffic, we have used two popular specifications, namely the G3-PLC–“G3 Power Line communication” and PRIME–acronym for “PoweRline Intelligent Metering Evolution”, to simulate the characteristics of a system that is widely used in practice. The results can be an insight for further development of the Information and Communication Technology (ICT) systems that control and monitor the Low Voltage (LV) distribution grid. The paper presents an analysis towards identifying the needs of distribution networks with respect to telecommunication data as well as the main parameters that can affect the Inverse Fast Fourier Transform (IFFT) system performance. Identifying such parameters is consequently beneficial to designing more efficient ICT systems for Advanced Metering Infrastructure.

scholarly journals A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5650
Author(s):  
Jenniffer S. Guerrero-Prado ◽  
Wilfredo Alfonso-Morales ◽  
Eduardo F. Caicedo-Bravo
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Smart Grids ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Advanced Metering Infrastructure ◽  
Data Interoperability ◽  
Architecture Model ◽  
Data Framework ◽  
Advanced Metering

The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP’s markets was less than 5%. In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

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