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Advances in Smart Grid Architectures: Sensor Technologies and Intelligent Monitoring Systems

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

Deadline for manuscript submissions: 20 November 2026 | Viewed by 1691

Special Issue Editors

Prof. Dr. El Hadj Miliani

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Guest Editor
IFP School, IFP New Energy, 232 Avenue Napoléon Bonaparte, 92500 Rueil-Malmaison, France
Interests: transportation electrification; power train; propulsion; onboard energy conversion; propulsion control; powertrain architecture; onboard energy sources
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arash Farnoosh

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Guest Editor
IFP School, IFP New Energy, 232 Avenue Napoléon Bonaparte, 92500 Rueil-Malmaison, France
Interests: energy engineering; energy economics; energy geopolitics

Special Issue Information

Dear Colleagues,

Introduction: Recent advances in smart grid architectures have significantly improved energy distribution and system reliability. Sensor technologies now enable real-time data collection across transmission and distribution networks. These sensors enhance fault detection, load forecasting, and demand-side management. Intelligent monitoring systems, powered by AI and machine learning, allow predictive maintenance and efficient grid optimization. Together, these innovations reduce energy losses and support the integration of renewable sources. As a result, modern smart grids are becoming more resilient, adaptive, and sustainable.

Scope: Sensor technology serves as the foundational layer of smart grid architectures by enabling real-time monitoring of electrical parameters across the grid. These systems rely on accurate, high-frequency sensor data to ensure adaptive, efficient, and resilient grid operations.

Topics: overview of smart grid architectures and evolution; types and roles of sensor technologies in smart grids; real-time data acquisition and communication protocols; intelligent monitoring systems and ai-driven analytics, fault detection, isolation, and self-healing capabilities; cybersecurity challenges in sensor-integrated smart grids; integration of renewable energy through adaptive monitoring; case studies and implementation challenges.

Prof. Dr. El Hadj Miliani
Prof. Dr. Arash Farnoosh
Guest Editors

Manuscript Submission Information

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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 architecture
  • sensor technologies
  • intelligent monitoring systems
  • real-time data acquisition
  • artificial intelligence in power systems
  • predictive maintenance
  • grid resilience
  • state estimation
  • renewable energy integration

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Published Papers (2 papers)

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24 pages, 674 KB  
Article
Data-Driven Parameter Identification of Synchronous Generators: A Three-Stage Framework with State Consistency and Grid Decoupling
by Rasool Peykarporsan, Tharuka Govinda Waduge, Tek Tjing Lie and Martin Stommel
Sensors 2026, 26(7), 2024; https://doi.org/10.3390/s26072024 - 24 Mar 2026
Viewed by 567
Abstract
As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable [...] Read more.
As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, Xd, Xq, Xd′, Xq′, Tdo′, Tqo′) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment. Full article
(This article belongs to the Special Issue Advances in Smart Grid Architectures: Sensor Technologies and Intelligent Monitoring Systems)
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41 pages, 3852 KB  
Systematic Review
Hybrid AI Models for Short-Term Photovoltaic Forecasting: A Systematic Review of Architectures, Performance, and Deployment Challenges
by Joan M. Saltos, M. Gabriela Intriago Cedeño, Ney R. Balderramo Velez, Germán T. Ramos León and A. Cano-Ortega
Sensors 2026, 26(6), 1793; https://doi.org/10.3390/s26061793 - 12 Mar 2026
Viewed by 816
Abstract
The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack [...] Read more.
The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack of systematic compilation of their structures, effectiveness, and readiness for use in real-world applications. This paper provides a detailed analysis of 58 peer-reviewed articles (2020–2025) focused on hybrid models for short-term (1–24 h) solar photovoltaic power forecasting. We propose an innovative classification that groups hybrids into four categories: AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%). Our research indicates that weather conditions (34%) and historical photovoltaic energy records (32%) are the most frequent inputs, and that optimized hybrids and those using decomposition achieve the best balance between effectiveness and computational efficiency. From a geographical perspective, the study focuses mainly on the United States (29%) and China (22%), suggesting that more extensive climate validation is crucial. Essentially, we have identified ongoing obstacles to implementation, such as high computational costs, data quality issues, and gaps in interpretation. In addition, we present a plan for future research focusing on hybrid architectures that are lightweight, understandable, and interactive with the grid. This analysis provides a thorough assessment of the current landscape and a strategic framework to guide the creation of operational forecasting systems capable of supporting highly solar-integrated grids. Full article
(This article belongs to the Special Issue Advances in Smart Grid Architectures: Sensor Technologies and Intelligent Monitoring Systems)
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