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Intelligent Monitoring and Modeling of Electrical Systems in Renewable-Powered Microgrids: Challenges and Solutions for Strengthening Energy Security and Blackout Prevention

A Special Issue of Energies (ISSN 1996-1073) belonging to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 3221

Editor

Dr. Dragos Pasculescu

E-Mail Website
Guest Editor
Automation, Computer, Electrical and Power Department, University of Petrosani, 332006 Petrosani, Romania
Interests: smart grid; power systems; electricity; power grid; electrical safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid transformation of the global energy sector has made renewable-powered microgrids a central component of sustainable development and the transition to low-carbon societies. By enabling the integration of distributed renewable sources such as photovoltaic panels, wind turbines, and energy storage systems, microgrids provide flexibility, local autonomy, and environmental benefits. However, the variability and intermittency of renewables introduce significant challenges to system stability, reliability, and supply continuity. In this context, intelligent monitoring and advanced modeling of electrical systems emerge as critical tools for ensuring the secure, efficient, and resilient operation of modern microgrids.

The Special Issue, “Intelligent Monitoring and Modeling of Electrical Systems in Renewable-Powered Microgrids: Challenges and Solutions for Strengthening Energy Security and Blackout Prevention”, is designed to serve as a reference point for researchers, engineers, policymakers, and industry stakeholders. Its aim is twofold: first, to provide potential authors with a platform to share cutting-edge methodologies, solutions, and case studies; second, to clarify the broader context of how intelligent monitoring and predictive modeling can directly contribute to addressing global concerns such as energy security and blackout prevention.

The key objectives of this Special Issue include exploring novel strategies for real-time monitoring, digital twin applications, predictive analytics, and fault detection in microgrids. Contributions are also expected to highlight optimization techniques for load management, energy storage integration, and demand-side flexibility, all of which are essential for enhancing system resilience. Given the growing importance of cybersecurity in interconnected and digitalized power systems, papers addressing risk assessment, secure data communication, and protection against cyberattacks are particularly encouraged.

Another essential aspect is the role of microgrids in energy security and blackout resilience. Intelligent systems allow seamless transitions between grid-connected and islanded modes, the effective utilization of storage, and adaptive control strategies to minimize the risk of cascading failures. This Special Issue, therefore, seeks works that not only highlight technological innovation, but also address regulatory, economic, and societal dimensions of resilient renewable-based power systems.

We welcome a broad range of contributions, including original research articles, reviews, and practical case studies, covering topics such as intelligent monitoring and diagnostics, predictive modeling and digital twins, energy optimization and smart control, blackout mitigation strategies, energy storage system integration, cybersecurity in microgrids, and policy frameworks supporting energy security.

By bringing together multidisciplinary perspectives, this Special Issue aims to clarify the state of the art, highlight open challenges, and inspire innovative solutions. Ultimately, it seeks to provide the scientific and industrial communities with a comprehensive understanding of how intelligent monitoring and modeling can accelerate the transition toward secure, sustainable, and blackout-resilient renewable-powered microgrids.

Dr. Dragos Pasculescu
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 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. Energies 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

  • renewable-powered microgrids
  • intelligent monitoring
  • predictive modeling
  • energy security
  • blackout prevention
  • fault detection and diagnostics
  • energy storage integration
  • cybersecurity in power systems
  • resilient energy networks
  • grid stability
  • blackout resilience

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Published Papers (3 papers)

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Research

27 pages, 8209 KB  
Article
A Dual-View Mixup-ResNet Method for Intelligent Monitoring and Fault Diagnosis of Cable Sheath Circulating Current Signals
by Haiqi Yang, Jinwei Mao, Bo Zhang, Jize He and Xiaoyu Liang
Energies 2026, 19(14), 3290; https://doi.org/10.3390/en19143290 - 13 Jul 2026
Viewed by 398
Abstract
In renewable-powered distribution systems and microgrids, reliable cable condition monitoring is essential for operational security and early fault detection. Sheath circulating current signals provide valuable information for identifying grounding abnormalities and incipient faults, but their diagnosis is difficult because the signals exhibit strong [...] Read more.
In renewable-powered distribution systems and microgrids, reliable cable condition monitoring is essential for operational security and early fault detection. Sheath circulating current signals provide valuable information for identifying grounding abnormalities and incipient faults, but their diagnosis is difficult because the signals exhibit strong inter-phase coupling, and fault samples are limited in practice. This study proposes a dual-view Mixup-ResNet framework for fault diagnosis of cable sheath circulating current signals. Specifically, physical-range normalization is employed to retain magnitude-related fault information and inter-phase proportional relationships, while sample-wise z-score normalization is used to emphasize waveform morphology. These two complementary views are concatenated to form a six-channel input for a lightweight one-dimensional residual network, which is trained with Mixup, label smoothing, dropout, and cosine annealing. On an ATP-EMTP-generated eight-class dataset, the proposed method achieves an average accuracy of 91.50%, a weighted F1-score of 91.69%, and a macro F1-score of 91.69% under a unified 5 × 5 repeated stratified cross-validation protocol. Additional tests under 12% relative Gaussian noise show that the method maintains competitive Gaussian-noise tolerance within the tested condition, although broader field disturbances remain to be further validated. These findings suggest that the proposed method has potential for small-sample fault diagnosis of cable sheath circulating current signals and provides a preliminary basis for intelligent cable condition monitoring. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Modeling of Electrical Systems in Renewable-Powered Microgrids: Challenges and Solutions for Strengthening Energy Security and Blackout Prevention)
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24 pages, 3677 KB  
Article
Research on Early Aging Fault Identification of Photovoltaic DC Combiner System
by Yu Zou, Kailong Chen, Huawei He, Zile Li and Jiaxun Teng
Energies 2026, 19(5), 1273; https://doi.org/10.3390/en19051273 - 4 Mar 2026
Viewed by 451
Abstract
In order to detect typical faults in the photovoltaic Direct current (DC) system, this paper first compared and analyzed the frequency domain responses of two typical faults in the photovoltaic DC system at different insulation aging stages based on the early insulation aging [...] Read more.
In order to detect typical faults in the photovoltaic Direct current (DC) system, this paper first compared and analyzed the frequency domain responses of two typical faults in the photovoltaic DC system at different insulation aging stages based on the early insulation aging theory and the insulation aging characteristics of DC transmission cables. A fault identification method based on the harmonic trajectory of the primary and secondary switch frequencies for pole-to-pole short-circuit faults was proposed. The feasibility of this fault identification method was verified through the controller algorithm and combined with the hardware experimental platform. This method utilizes basic mathematical theorems, and the code configuration is easy to implement. At the same time, adjusting the sliding-window coefficient and the threshold coefficient can change the sensitivity and false-alarm rate of the algorithm, achieving different levels of fault-identification accuracy and leading to the best results. A 90% accuracy and a false-alarm rate lower than 10% can be achieved, opening up new ideas and methods for photovoltaic fault identification. Experimental results show that this method has a fast fault identification speed, with an identification time ranging from 10 to 20 ms, and can achieve different levels of identification accuracy by flexibly adjusting the control parameters. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Modeling of Electrical Systems in Renewable-Powered Microgrids: Challenges and Solutions for Strengthening Energy Security and Blackout Prevention)
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44 pages, 2712 KB  
Article
Intelligent Modeling of PV–BESS Microgrids for Enhanced Stability, Cyber–Physical Resilience and Blackout Prevention
by Dragos Pasculescu, Simona Riurean, Mila Ilieva-Obretenova, Teodora Lazar, Adina Milena Tatar and Nicolae Daniel Fita
Energies 2026, 19(1), 148; https://doi.org/10.3390/en19010148 - 26 Dec 2025
Cited by 9 | Viewed by 1650
Abstract
This paper proposes and validates a method for assessing the resilience of cyber–physical microgrids integrating Photovoltaic (PV) generation and Battery Energy Storage Systems (BESS). The approach combines two operational performance indicators—Voltage Deviation Index (VDI) and Energy Not Supplied (ENS)—with a composite resilience index [...] Read more.
This paper proposes and validates a method for assessing the resilience of cyber–physical microgrids integrating Photovoltaic (PV) generation and Battery Energy Storage Systems (BESS). The approach combines two operational performance indicators—Voltage Deviation Index (VDI) and Energy Not Supplied (ENS)—with a composite resilience index that captures recovery dynamics following physical and cyber disturbances. The method is implemented in MATLAB Simulink R2022b on the IEEE 33-bus feeder, with PV at bus 6 and a BESS at bus 18. Two stress scenarios are analyzed: (i) loss of the main supply at bus 2 and (ii) a cyber-induced communication failure that triggers local (fallback) operation. Compared with the base case, the proposed strategy reduces VDI by approximately 27% and ENS by 12%, demonstrating significantly improved resilience without noticeable performance penalties. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Modeling of Electrical Systems in Renewable-Powered Microgrids: Challenges and Solutions for Strengthening Energy Security and Blackout Prevention)
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