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Electronics
Special Issues
Monitoring and Analysis for Smart Grids
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Monitoring and Analysis for Smart Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2732

Special Issue Editors

Dr. Mukesh Gautam

E-Mail Website
Guest Editor
Optimization and Control Group, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Interests: power system resiliency; power system optimization and control; machine learning applications; renewable energy integration
Dr. Roshan Sharma

E-Mail Website
Guest Editor
Commonwealth Edison Company (ComEd), Chicago, IL 60618, USA
Interests: control and integration of renewable energy resources; microgrids; power system dynamics; power system modeling and control

Special Issue Information

Dear Colleagues,

The integration of photovoltaic (PV) systems, wind energy, electric vehicles, among others, into the modern power grid has significantly complicated its monitoring and analysis due to the uncertainty of renewable power generation. This paradigm shift necessitates the proposal or adoption of new measures for effectively managing and understanding the smart grid.

In this context, this Special Issue, "Monitoring and Analysis for Smart Grids", stands poised to chart a significant course in advancing our comprehension and practical applications within this evolving domain.

The smart grid, positioned at the intersection of technological innovation and sustainable energy, represents a pivotal nexus in our contemporary landscape. Its evolution and optimal functioning are not only crucial for meeting escalating energy demands, but are also imperative for ensuring environmental sustainability.

Aligned with the overarching scope of the journal, this Special Issue aims to spotlight the intersection of monitoring and analysis within the smart grid paradigm. Our goal is to curate a diverse array of contributions that elucidate and catalyze advancements and innovations within this vital sphere.

Themes worth exploring encompass, but are not limited to, the following:

  • Real-time monitoring technologies for grid performance assessment;
  • Data analytics and predictive modeling for grid optimization;
  • Integration of renewable energy sources and bolstering grid resilience;
  • Cybersecurity frameworks within smart grid infrastructure;
  • Advanced sensor networks and their application in grid monitoring;
  • Policy implications and regulatory frameworks shaping smart grid development.

We also invite original research articles and comprehensive reviews that uncover new dimensions, explore novel methodologies, or present insightful analyses within these areas. Through this collaborative endeavor, we aim to enrich our collective knowledge and foster innovative solutions for the challenges and opportunities inherent in the evolution of the smart grid.

We eagerly anticipate the diverse and illuminating contributions that will enrich this Special Issue.

Dr. Mukesh Gautam
Dr. Roshan Sharma
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. Electronics 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 2400 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
  • grid monitoring
  • data analytics
  • grid optimization
  • renewable integration
  • grid resilience
  • cybersecurity for smart grid
  • sensors networks

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 3971 KiB  
Article
An Extra-High Voltage Test System for Transmission Expansion Planning Studies Considering Single Contingency Conditions
by Bhuban Dhamala and Mona Ghassemi
Electronics 2024, 13(19), 3937; https://doi.org/10.3390/electronics13193937 - 5 Oct 2024
Cited by 2 | Viewed by 989
Abstract
This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using [...] Read more.
This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically. Full article
(This article belongs to the Special Issue Monitoring and Analysis for Smart Grids)
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27 pages, 5963 KiB  
Article
Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids
by Ozgur Alaca, Emilio Carlos Piesciorovsky, Ali Riza Ekti, Nils Stenvig, Yonghao Gui, Mohammed Mohsen Olama, Narayan Bhusal and Ajay Yadav
Electronics 2024, 13(18), 3663; https://doi.org/10.3390/electronics13183663 - 14 Sep 2024
Viewed by 1173
Abstract
This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an [...] Read more.
This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions. Full article
(This article belongs to the Special Issue Monitoring and Analysis for Smart Grids)
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