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Electronics
Special Issues
Microgrids: Innovations in Fault Detection, Security, and Addressing Challenges
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Microgrids: Innovations in Fault Detection, Security, and Addressing Challenges

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

Deadline for manuscript submissions: 15 February 2026 | Viewed by 761

Special Issue Editor

Dr. Tala Talaei Khoei

E-Mail Website
Guest Editor
The Roux Institute, Northeastern University, Portland, ME 04103, USA
Interests: intrusion detection systems; artificial intelligence; smart grid; supervised learning; unsupervised learning; data pre-processing; machine learning; semi-supervised learning; optimization techniques; reinforcement learning

Special Issue Information

Dear Colleagues,

This Special Issue aims to spotlight recent advancements in microgrid technology, with a particular focus on enhancing fault detection mechanisms, strengthening security protocols, and addressing the multifaceted challenges associated with microgrid deployment.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Novel fault detection algorithms for microgrid systems;
  • Robust cybersecurity measures to ensure the integrity of microgrid operations;
  • In-depth exploration of challenges and practical solutions in the implementation of microgrid technology;
  • Explore cutting-edge fault detection strategies tailored for microgrids;
  • Examine innovative security measures to fortify the resilience of microgrid systems;
  • Investigate and discuss challenges inherent in the deployment and operation of microgrids, providing insights into potential solutions.

This Special Issue is designed for researchers, academics, industry professionals, and policymakers in the fields of electrical engineering, cybersecurity, and renewable energy, fostering collaboration and knowledge exchange. Contributors are invited to submit original research articles, reviews, and case studies adhering to the journal's guidelines. A rigorous peer review will be conducted to ensure the quality and relevance of the submissions.

We look forward to receiving innovative research contributions that will contribute to the advancement of microgrid technology and its associated challenges.

Dr. Tala Talaei Khoei
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. 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

  • microgrids
  • fault detection
  • security measures
  • renewable energy integration
  • smart grid technologies
  • resilient power distribution
  • cybersecurity
  • advanced algorithms
  • energy management
  • grid resilience
  • grid fairness
  • grid safety
  • distributed energy resources
  • challenges in microgrid deployment
  • fault tolerance
  • sustainable energy
  • decentralized power systems
  • grid reliability
  • renewable energy sources
  • energy independence
  • power quality
  • case studies in microgrid implementation

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Published Papers (1 paper)

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Research

27 pages, 1513 KB  
Article
Accurate Fault Classification in Wind Turbines Based on Reduced Feature Learning and RVFLN
by Mehmet Yıldırım and Bilal Gümüş
Electronics 2025, 14(19), 3948; https://doi.org/10.3390/electronics14193948 - 7 Oct 2025
Viewed by 401
Abstract
This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend [...] Read more.
This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article
(This article belongs to the Special Issue Microgrids: Innovations in Fault Detection, Security, and Addressing Challenges)
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