Metering architecture of smart grid

Kaur, Inderpreet

doi:10.1016/b978-0-12-824555-2.00030-7

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The Extreme Edge computing scheme refers to the outer-most layers within a cloud computing architecture, i.e., those nearest either the end users or things under monitoring/control. On the basis of the Extreme Edge use cases, the classical approach pro-vided by the IoT-supported Advanced Metering Infrastructure (AMI) [Kaur, 2021] has attracted particular attention in the area of our research. In AMI-IoT, the smart meters transmit through an Internet connection, (time-to-time), and all the electrical readings are delivered to a known remote utility in the SCADA system which is generally running in a central cloud server.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) architecture

Marques,

Eugênio,

Bastos

et al. 2023

JISA

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This paper presents a study on the performance of Machine Learning techniques for the task of determining each of the electronics systems connected within the same electrical network, in the Internet of Smart Grid Things (IoSGT) architecture. This is regarded as an ecosystem which has cutting-edge technologies that work together to enable advanced SG applications, and run in core/edge cloud datacenters connected to an underlying IoT network infrastructure. This identification was carried out by analyzing traditional energy measures (i.e., voltage, current and power) through ML-based techniques. The analysis of regular energy measurements is required as a means of ensuring compatibility with legacy /smart meters, without the need to exchange them for new ones or make updates. The data was obtained by using a smart meter built by our group, and was processed and validated in the IoSGT edge-cloud.

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Section: Introductionmentioning

confidence: 99%

Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) architecture

Marques,

Eugênio,

Bastos

et al. 2023

JISA

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Privacy-Preserving Federated Learning for Value-Added Service Model in Advanced Metering Infrastructure

Zhang

Córdoba‐Pachón

Guo

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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The Role of Electromobility in the Energy-Related Smart Grids

Krizmanič

Groznik

2023

Handbook of Research on Promoting Sustainable Public Transportation Strategies in Urban Environments

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The chapter describes the major components of the power grid. It then describes and explains the trends in the consumption and generation of electricity, with a particular focus on increasing the share of renewable energy sources. The concept of the smart grid is then described, including a discussion of the smart meter and the prosumer concept. The second part of the chapter is dedicated to electromobility. This section describes the main characteristics of electric vehicles and the trend of their diffusion, battery technologies, charging stations, smart charging, and the vehicle-to-grid concept.

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Metering architecture of smart grid

Cited by 3 publications

References 19 publications

Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) architecture

Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) architecture

Privacy-Preserving Federated Learning for Value-Added Service Model in Advanced Metering Infrastructure

The Role of Electromobility in the Energy-Related Smart Grids

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