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Advanced Communication and Computing Technologies for Smart Grid
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Advanced Communication and Computing Technologies for Smart Grid

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 13963

Special Issue Editor

Dr. Yi Liu

E-Mail Website
Guest Editor
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: wireless communication networks; cooperative communications; smart grid; intelligent edge computing

Special Issue Information

Dear Colleagues,

The smart grid is considered to be an intelligent electricity control system that integrates all elements connected to the electrical grid with an information infrastructure, offering numerous benefits for both the providers and consumers of electricity. The sheer number of sensors, smart meters, and control devices for building the smart grid requires reliable, transparent, cost-effective, and easy-update communication and computing infrastructure. Therefore, the advanced communication and computing technologies, such as wireless communications with Artificial Intelligence (AI), 6G, mobile edge computing, space–air–ground integrated network, etc., are good candidates for the development of future smart grids.

This Special Issue is addressed to all types of sensors and related systems designed for smart grids.

Dr. Yi Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart grid
  • next-generation communications
  • 6G
  • mobile edge computing
  • space-air-ground integrated network
  • Artificial Intelligence (AI) application

Published Papers (6 papers)

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Research

14 pages, 1418 KiB  
Article
Spot Detection for Laser Sensors Based on Annular Convolution Filtering
by Lingjiang Li, Maolin Li, Weijun Sun, Zhenni Li and Zuyuan Yang
Sensors 2023, 23(8), 3891; https://doi.org/10.3390/s23083891 - 11 Apr 2023
Viewed by 1553
Abstract
Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, [...] Read more.
Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, we propose a novel method called annular convolution filtering (ACF). In our method, the region of interest (ROI) in the spot image is first searched by using the statistical properties of pixels. Then, the annular convolution strip is constructed based on the energy attenuation property of the laser and the convolution operation is performed in the ROI of the spot image. Finally, a feature similarity index is designed to estimate the parameters of the laser spot. Experiments on three datasets with different kinds of background light show the advantages of our ACF method, with comparison to the theoretical method based on international standard, the practical method used in the market products, and the recent benchmark methods AAMED and ALS. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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18 pages, 5323 KiB  
Article
A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse
by Mohammad (Behdad) Jamshidi, Salah I. Yahya, Leila Nouri, Hamed Hashemi-Dezaki, Abbas Rezaei and Muhammad Akmal Chaudhary
Sensors 2023, 23(7), 3775; https://doi.org/10.3390/s23073775 - 06 Apr 2023
Cited by 12 | Viewed by 2125
Abstract
Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are [...] Read more.
Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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18 pages, 2260 KiB  
Article
Energy Contour Forecasting Optimization with Smart Metering in Distribution Power Networks
by Cristian-Dragoș Dumitru, Adrian Gligor, Ilie Vlasa, Attila Simo and Simona Dzitac
Sensors 2023, 23(3), 1490; https://doi.org/10.3390/s23031490 - 29 Jan 2023
Cited by 2 | Viewed by 1870
Abstract
Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption [...] Read more.
Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity—industrial or commercial—and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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14 pages, 4030 KiB  
Article
SSA-VMD for UWB Radar Sensor Vital Sign Extraction
by Huimin Yu, Wenjun Huang and Baoqiang Du
Sensors 2023, 23(2), 756; https://doi.org/10.3390/s23020756 - 09 Jan 2023
Cited by 4 | Viewed by 2012
Abstract
The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) [...] Read more.
The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) radar sensor is suitable for this application because of its strong penetration ability, high range resolution and low average power consumption. However, due to weak heartbeat amplitude and measurement noise, the accurate measurement of the target heart rate is a challenge. In this paper, singular spectrum analysis (SSA) is proposed to reconstruct the eigenvalues of noisy vital signs to eliminate noise peaks around the heartbeat rate; combined with the variational modal decomposition (VMD), the target vital signs can be extracted with high accuracy. The experiment confirmed that the target vital sign information can be extracted with high accuracy from ten subjects at different distances, which can play an important role in short distance human detection and vital sign monitoring. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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19 pages, 723 KiB  
Article
A Local Electricity and Carbon Trading Method for Multi-Energy Microgrids Considering Cross-Chain Interaction
by Xiaoqing Zhong, Yi Liu, Kan Xie and Shengli Xie
Sensors 2022, 22(18), 6935; https://doi.org/10.3390/s22186935 - 14 Sep 2022
Cited by 7 | Viewed by 2044
Abstract
The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, [...] Read more.
The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, excessive electricity and carbon allowance of a microgrid can be shared with other microgrids that require them. A local electricity trading problem and a local carbon trading problem are formulated for multi-energy microgrids using the Nash bargaining theory. Each Nash bargaining problem can be decomposed into two subproblems, including an energy/carbon scheduling problem and a payment bargaining problem. By solving the subproblems of the Nash bargaining problems, the traded amounts of electricity/carbon allowance between microgrids and the corresponding payments will be determined. In addition, to enable secure information interactions and trading payments, we introduce an electricity blockchain and a carbon blockchain to record the trading data for microgrids. The novelty of the usage of the blockchain technology lies in using a notary mechanism-based cross-chain interaction method to achieve value transfer between blockchains. The simulation results show that the proposed local electricity and carbon trading method has great performance in lowering total payments and carbon emissions for microgrids. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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10 pages, 332 KiB  
Communication
Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data
by Zhoujun Ma, Hong Zhu, Zhuohao He, Yue Lu and Fuyuan Song
Sensors 2022, 22(14), 5331; https://doi.org/10.3390/s22145331 - 16 Jul 2022
Cited by 3 | Viewed by 3236
Abstract
Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose [...] Read more.
Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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