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Sensors Technology and Data Analytics Applied in Smart Grid
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Sensors Technology and Data Analytics Applied in Smart Grid

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

Deadline for manuscript submissions: 25 May 2024 | Viewed by 3772

Special Issue Editors

Dr. Enrique Personal

E-Mail Website
Guest Editor
Electronic Technology Department, Escuela Politécnica Superior, University of Seville, St. Virgen de Africa, 7, 41011 Seville, Spain
Interests: fault location; power distribution network; power delivery; underground distribution system
Special Issues, Collections and Topics in MDPI journals
Dr. Michela Robba

E-Mail Website
Guest Editor
DIBRIS—Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, 16145 Genova, Italy
Interests: optimization; control; planning and control of smart grids; electric vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Parejo

E-Mail
Guest Editor
Electronic Technology Department, Escuela Politécnica Superior, University of Seville, St. Virgen de Africa, 7, 41011 Seville, Spain
Interests: power systems; renewable energy; generation and demand forecasting; demand response; flexibility; smart grid

Special Issue Information

Dear Colleagues,

At present, as a response to the resilience demands of modern networks and the commitment to decarbonize our society, the smart grid paradigm is being adopted by power utilities, pursuing cleaner and more efficient management. A proof of this is the evolution that the network topology has experienced, in which the deployment of distributed generation, storage, and controllable load systems has grown, resulting in self-controlled microgrids, virtual power plants and, more generally, smart local energy communities. Furthermore, these new architectures of decentralized power infrastructures require more resilience and faster communication systems that support the demands of this network philosophy. Thus, in this scenario, new approaches in sensor deployments and new algorithms to analyze their information are becoming an essential tool to support this network growth. Additionally, due to the wide areas occupied by this type of power grids and the variety of systems involved, the use of communication systems and protocols that allow them to guarantee the interoperability as well as the integrity and coherence of the information is also essential.

In this sense, in order to face the aforementioned challenges, we propose this Special Issue, titled “Sensors Technology and Data Analytics Applied in Smart Grid”. Under this title, we expect high-quality previously unpublished papers focused on the design and use of new sensor architectures for smart grid applications, which include but are not limited to the following topics:

  • Advanced metering infrastructures;
  • Wide-area monitoring, protection, and control systems;
  • Demand-side management systems;
  • Smart home and smart building sensors and IoT applications;
  • Energy monitoring and energy disaggregation;
  • Forecasting and optimization for energy management;
  • Technical and non-technical losses estimation;
  • Electric vehicle as a sensor in energy management;
  • Energy flexibility market devices;
  • Monitoring systems for asset management;
  • Cyber-physical systems in energy applications;
  • Communication protocols and standard;
  • Transactive energy and blockchain applications for data sensor integrity.

Dr. Enrique Personal
Dr. Michela Robba
Dr. Antonio Parejo
Guest Editors

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 sensors
  • advanced metering infrastructures
  • demand-side management
  • microgrid and virtual power plant management
  • energy flexibility market
  • monitoring systems for asset management

Published Papers (3 papers)

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Research

23 pages, 4007 KiB  
Article
Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions
by Reinier Herrera-Casanova, Arturo Conde and Carlos Santos-Pérez
Sensors 2024, 24(3), 882; https://doi.org/10.3390/s24030882 - 29 Jan 2024
Cited by 1 | Viewed by 716
Abstract
Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from [...] Read more.
Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions. Full article
(This article belongs to the Special Issue Sensors Technology and Data Analytics Applied in Smart Grid)
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35 pages, 1205 KiB  
Article
Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification within a Power Transmission System
by Jonathan D. Boyd, Joshua H. Tyler, Anthony M. Murphy and Donald R. Reising
Sensors 2024, 24(2), 483; https://doi.org/10.3390/s24020483 - 12 Jan 2024
Viewed by 511
Abstract
As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power [...] Read more.
As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an average accuracy of 99%. Continuous nominal signal data analysis was performed using an approach called the cyclic histogram. The cyclic histogram process is intended to be integrated into the digital fault recorders themselves in order to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increasing the reliability of the transmission system by providing near real-time detection and identification of disturbances as well as prevention of problems before they occur. Full article
(This article belongs to the Special Issue Sensors Technology and Data Analytics Applied in Smart Grid)
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25 pages, 1679 KiB  
Article
A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities
by Giovanni Cicceri, Giuseppe Tricomi, Luca D’Agati, Francesco Longo, Giovanni Merlino and Antonio Puliafito
Sensors 2023, 23(9), 4549; https://doi.org/10.3390/s23094549 - 07 May 2023
Cited by 2 | Viewed by 1681
Abstract
The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with [...] Read more.
The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach’s effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape. Full article
(This article belongs to the Special Issue Sensors Technology and Data Analytics Applied in Smart Grid)
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