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Advances in Sensors and Metering Solutions for Smart Grids
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Advances in Sensors and Metering Solutions for Smart Grids

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1335

Editors

Prof. Dr. Silvia Sangiovanni

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Guest Editor
Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy
Interests: digital signal processing applied to electrical measurements; power quality; clinical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Federico Carere

E-Mail Website
Guest Editor
Department of Information Science and Technology, Telematic University Pegaso, 80143 Naples, Italy
Interests: distributed measurement systems for modern electric grids; non-destructive testing based on eddy-current techniques; sensor realization and characterization
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Sardellitti

E-Mail Website
Guest Editor
Department of Engineering and Science, Universitas Mercatorum, Piazza Mattei 10, 00186 Rome, Italy
Interests: non-destructive testing; eddy current testing; electromagnetic engineering; instrumentation; sensors; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global transition toward Smart Grids requires networks that are increasingly observable, controllable, and automated. At the core of this transformation are advanced sensing and metering solutions, which provide the high-quality data necessary for real-time monitoring. As distributed energy resources become widespread, traditional infrastructures face critical challenges regarding power quality, synchronization, and data analytics.

This Special Issue addresses the convergence of advanced instrumentation and electrical grids modernization. It covers the full measurement chain, from physical transducers to signal processing and system integration, with a particular focus on sensors design, development, and applications. We invite contributions focusing on novel sensor designs, distributed measurement architectures, and measurement techniques that emphasize metrological performance and uncertainty analysis. The ultimate goal is to propose optimized sensing technologies and novel measurement systems to enable active management and control of future power grids.

Prof. Dr. Silvia Sangiovanni
Dr. Federico Carere
Dr. Alessandro Sardellitti
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 grids
  • smart metering
  • distribution networks
  • advanced metering infrastructure
  • power quality
  • distributed measurement systems
  • uncertainty evaluation
  • smart sensors
  • flexibility
  • voltage regulation

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Published Papers (3 papers)

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Research

25 pages, 994 KB  
Article
One-Cycle Windowed-DFT Harmonic Estimation with Spectral-Interference Compensation
by Chemseddine Allioua, Alessandro Mingotti, Roberto Tinarelli and Lorenzo Peretto
Sensors 2026, 26(14), 4362; https://doi.org/10.3390/s26144362 - 9 Jul 2026
Abstract
Accurate harmonic estimation at the per-cycle timescale is increasingly required in modern power-quality (PQ) monitoring, where fast-varying distortion sources demand high temporal resolution. However, when harmonic phasors are estimated from a single cycle using windowed discrete Fourier transform techniques, off-nominal fundamental frequency introduces [...] Read more.
Accurate harmonic estimation at the per-cycle timescale is increasingly required in modern power-quality (PQ) monitoring, where fast-varying distortion sources demand high temporal resolution. However, when harmonic phasors are estimated from a single cycle using windowed discrete Fourier transform techniques, off-nominal fundamental frequency introduces spectral interference between harmonics, leading to systematic amplitude and phase errors that conventional correction methods cannot remove. This paper presents a lightweight, non-iterative harmonic estimation module designed to operate on fixed-rate, one-cycle data streams. The method leverages a frequency estimate provided by an external tracker to explicitly model the spectral interference induced by windowing under off-nominal conditions. By formulating this effect as a linear mixing process, the proposed approach applies an algebraic inversion to recover unbiased harmonic phasors without requiring adaptive resampling, variable window lengths, or modifications to the acquisition system. The module is designed as a plugin component compatible with existing PQ processing chains and shared sampled-value architectures. Experimental validation across frequency sweeps, Monte Carlo noise trials, and dynamic streaming scenarios demonstrates machine-precision accuracy in ideal conditions and noise-limited performance in realistic settings. Compared to iterative alternatives, the proposed solution achieves equivalent accuracy with a 484× reduction in computation time. A sensitivity analysis further quantifies the relationship between frequency-tracking accuracy and harmonic estimation error, providing practical guidelines for system integration. These results show that accurate, real-time harmonic estimation can be achieved from single-cycle data using fixed-rate acquisition, enabling improved monitoring and protection capabilities in modern power systems. Full article
(This article belongs to the Special Issue Advances in Sensors and Metering Solutions for Smart Grids)
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24 pages, 12170 KB  
Article
SA-YOLOv11s: A Slicing-Attention YOLOv11s with U-IoU for Oil Leakage Detection in Power Equipment
by Daoyuan Liu, Chenlei Liu, Zhijuan Wang, Shiji Zhang, Yulong Yang, Tong Zhao and Xiaolong Wang
Sensors 2026, 26(10), 3255; https://doi.org/10.3390/s26103255 - 20 May 2026
Viewed by 460
Abstract
To address the challenges of low detection accuracy and high missed detection rates in insulating oil leakage detection for power equipment—arising from small and densely distributed oil stains, structural occlusion, and complex background interference—this paper proposes a detection method based on an enhanced [...] Read more.
To address the challenges of low detection accuracy and high missed detection rates in insulating oil leakage detection for power equipment—arising from small and densely distributed oil stains, structural occlusion, and complex background interference—this paper proposes a detection method based on an enhanced YOLOv11s (You Only Look Once version 11 small) architecture. First, a dedicated dataset is constructed, encompassing four representative scenarios—small object detection, complex background, multi-object detection and equipment occlusion—to evaluate detection performance. Second, in terms of network design, a proposed attention module, SimAMWS (Simple Attention Module With Slicing), is introduced. This module enhances the model’s sensitivity to subtle and irregular oil stains by utilizing slicing operations and localized energy-based weighting. For bounding box regression, a U-IoU (Unified Intersection over Union) loss is adopted, which incorporates a dynamic scaling mechanism during training to enable the model to focus more effectively on high-quality candidate boxes—leading to improved localization accuracy, particularly suited to the characteristics of oil leakage. Finally, comparative experiments are conducted against mainstream object detectors including SSD (Single Shot MultiBox Detector), Faster R-CNN (Region-based Convolutional Neural Network), YOLOv5s, YOLOv8s, and the baseline YOLOv11s. The proposed method achieves an mAP@0.5 (mean Average Precision at IoU = 0.5) of 97.7% and an mAP@0.5:0.95 of 66.9%, with an inference speed of 96.4 FPS. These results demonstrate that the proposed model delivers higher detection accuracy while maintaining high inference efficiency, making it well-suited for real-time oil leak detection in power equipment and supporting the development of intelligent operation and maintenance systems in the power industry. Full article
(This article belongs to the Special Issue Advances in Sensors and Metering Solutions for Smart Grids)
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18 pages, 1111 KB  
Article
Uncertainty Effects on Smart Grid Services for Low-Voltage Distribution Networks
by Federico Carere, Tommaso Bragatto, Alberto Geri, Silvia Sangiovanni and Marco Laracca
Sensors 2026, 26(6), 1800; https://doi.org/10.3390/s26061800 - 12 Mar 2026
Viewed by 519
Abstract
This study investigates the impact of monitoring infrastructure characteristics (specifically sensor penetration and measurement accuracy) on the effectiveness of voltage regulation and congestion management within distribution networks. As distribution system operators transition toward active management, the integration of Distributed renewable Generation (DG) and [...] Read more.
This study investigates the impact of monitoring infrastructure characteristics (specifically sensor penetration and measurement accuracy) on the effectiveness of voltage regulation and congestion management within distribution networks. As distribution system operators transition toward active management, the integration of Distributed renewable Generation (DG) and demand response introduces significant physical and cyber-physical uncertainties. To address these challenges, a smart grid service framework has been employed to optimize flexibility resources from aggregated users and DG inverters through a genetic algorithm. The framework was tested on the IEEE European Low Voltage Test Feeder across various scenarios defined by distributed monitoring systems’ penetration and their measurement accuracy. Results show that sensor penetration has a dominant impact: increasing monitoring coverage from 0% to 100% raises the percentage of cases with fewer than one residual congestion from 46.2% to 91.9% (sensors with an accuracy class of 2%), reaching 97.9% with an accuracy class of 0.5%, while voltage violations are eliminated under full monitoring. These findings suggest that widespread sensor deployment, with a suitable measurement accuracy, is a fundamental prerequisite for reliable and efficient smart grid operation. Full article
(This article belongs to the Special Issue Advances in Sensors and Metering Solutions for Smart Grids)
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