Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensors and Data Analytics for the Smart Grid
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors and Data Analytics for the Smart Grid

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

Deadline for manuscript submissions: closed (30 October 2020) | Viewed by 5047

Special Issue Editor

Dr. Rune Hylsberg Jacobsen

E-Mail Website
Guest Editor
Department of Engineering, Aarhus University, Aarhus, Denmark
Interests: Internet of Things; smart grid communications; wireless IP networking

Special Issue Information

Dear Colleagues,

The rapid climate change is accelerating the need to significantly increase the penetration of renewable energy sources in power networks. Due to its intermittent nature, a large penetration of renewable energy generation may cause frequent power grid imbalances that jeopardize power quality and possibly result in blackouts.

Novel sensors and methods in data analytics enable innovative digital services to be deployed to improve the control and efficient usage and distribution of energy. By controlling the demand side, a solution to this problem can be the activation of flexible loads to provide a fast demand response when required. Smart networked IoT sensors may provide near real-time data that can be used to make cost-effective and environmentally friendly decisions. Algorithms and techniques based on statistical techniques and machine learning may provide the needed analytical model support to identify patterns and to offer a base for predicting variations of important features of the smart grid. However, security and privacy concerns may provide a barrier for a widespread deployment of sensors and open loop control. They must therefore be taken carefully into consideration in the design of system and services.

The aim of this Special Issue is to investigate aspects related to research in sensors and data analytics for the smart grid, in terms of design, optimization, communication, and control, including data collection and analytics. The topics of interest for this call include but are not limited to:

  • Smart sensors and sensor measurement networks;
  • Methods in data analytics for enabling a data-driven smart grid;
  • Novel services for smart grid control and optimization based on sensors and data analytics;
  • Communication design and implementation for efficient data collection;
  • Security and privacy aspects concerning sensors and data analytics in the smart grid;
  • Experiences from data-driven smart grid deployments.

Authors are also encouraged to submit their interdisciplinary contributions on these topics.

Assoc. Prof. Rune Hylsberg Jacobsen

Guest Editor

Dr. Rune Hylsberg Jacobsen
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.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 4404 KiB  
Article
Validity Evaluation Method Based on Data Driving for On-Line Monitoring Data of Transformer under DC-Bias
by Yuanda He, Qi Zhou, Sheng Lin and Liping Zhao
Sensors 2020, 20(15), 4321; https://doi.org/10.3390/s20154321 - 03 Aug 2020
Cited by 7 | Viewed by 2443
Abstract
The DC-bias monitoring device of a transformer is easily affected by external noise interference, equipment aging, and communication failure, which makes it difficult to guarantee the validity of monitoring data and causes great problems for future data analysis. For this reason, this paper [...] Read more.
The DC-bias monitoring device of a transformer is easily affected by external noise interference, equipment aging, and communication failure, which makes it difficult to guarantee the validity of monitoring data and causes great problems for future data analysis. For this reason, this paper proposes a validity evaluation method based on data driving for the on-line monitoring data of a transformer under DC-bias. First, the variation rule and threshold range of monitoring data for neutral point DC, vibration, and noise of the transformer under different working conditions are obtained through statistical analysis. Then, the data validity criterion of DC bias monitoring data is proposed to achieve a comprehensive evaluation of data validity based on data threshold, continuity, impact, and correlation. In addition, case studies are carried out on the real measured data of the DC bias magnetic monitoring system of a regional power grid by using this evaluation method. The results show that the proposed method can systematically and comprehensively evaluate the validity of the DC bias monitoring data and can judge whether the monitoring device fails to a certain extent. Full article
(This article belongs to the Special Issue Sensors and Data Analytics for the Smart Grid)
►▼ Show Figures

Figure 1

26 pages, 1110 KiB  
Article
A Heuristic to Create Prosumer Community Groups in the Social Internet of Energy
by Víctor Caballero, David Vernet and Agustín Zaballos
Sensors 2020, 20(13), 3704; https://doi.org/10.3390/s20133704 - 02 Jul 2020
Cited by 11 | Viewed by 2104
Abstract
Contrary to the rapid evolution experienced in the last decade of Information and Communication Technologies and particularly the Internet of Things, electric power distribution systems have remained exceptionally steady for a long time. Energy users are no longer passive actors; the prosumer is [...] Read more.
Contrary to the rapid evolution experienced in the last decade of Information and Communication Technologies and particularly the Internet of Things, electric power distribution systems have remained exceptionally steady for a long time. Energy users are no longer passive actors; the prosumer is expected to be the primary agent in the Future Grid. Demand Side Management refers to the management of energy production and consumption at the demand side, and there seems to be an increasing concern about the scalability of Demand Side Management services. The creation of prosumer communities leveraging the Smart Grid to improve energy production and consumption patterns has been proposed in the literature, and several works concerned with scalability of Demand Side Management services group prosumers to improve Demand Side Management services scalability. In our previous work, we coin the term Social Internet of Energy to refer to the integration between devices, prosumers and groups of prosumers via social relationships. In this work, we develop an algorithm to coordinate the different clusters we create using the clustering method by load profile compatibility (instead of similarity). Our objective is to explore the possibilities of the cluster-by-compatibility heuristic we proposed in our previous work. We perform experiments using synthetic and real datasets. Results show that we can obtain a global reduction in Peak-to-Average Ratio with datasets containing up to 200 rosumers and creating up to 6 Prosumer Community Groups, and imply that those Prosumer Community Groups can perform load rescheduling semi-autonomously and in parallel with each other. Full article
(This article belongs to the Special Issue Sensors and Data Analytics for the Smart Grid)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop