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AI-Driven Innovations in Power Electronics and Smart Grids: Enhancing Efficiency,...
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AI-Driven Innovations in Power Electronics and Smart Grids: Enhancing Efficiency, Reliability and Sustainability

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 3395

Special Issue Editor

Dr. Nur Haldar

E-Mail Website
Guest Editor
Faculty of Science and Engineering, School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Curtin University, Bentley, WA 6102, Australia
Interests: data science; cyber security; graph data analytics and modeling; social network analysis; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in artificial intelligence (AI) have significantly impacted various technological fields, including power electronics and smart grid systems. As energy demands continue to rise, there is an increasing need for intelligent solutions that optimize power generation, distribution, and consumption. AI-driven technologies offer the potential to revolutionize smart grids by enabling more efficient, reliable, and sustainable energy management. By integrating AI with power electronics, we can enhance real-time decision-making, improve fault detection, and optimize the integration of renewable energy sources, paving the way for the next generation of smart energy systems. This Special Issue seeks to explore these cutting-edge developments, bringing together research that bridges AI innovations with practical applications in the energy sector.

Releted Topics:

  • AI-driven optimization in power electronics and smart grids.
  • Machine learning for predictive maintenance in smart grids.
  • Neural networks in fault detection and diagnosis.
  • AI-enhanced power quality monitoring.
  • AI for intrusion detection and prevention in smart grid infrastructure.

Dr. Nur Haldar
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

  • AI-driven optimization
  • machine learning
  • fault detection
  • energy management systems
  • power quality monitoring
  • cybersecurity in smart grids

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Published Papers (3 papers)

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Research

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28 pages, 2070 KB  
Article
Enhancing Security and Applicability of Local LLM-Based Document Retrieval Systems in Smart Grid Isolated Environments
by Kiho Lee, Sumi Yang, Jaeyeong Jeong, Yongjoon Lee and Dongkyoo Shin
Electronics 2025, 14(17), 3407; https://doi.org/10.3390/electronics14173407 - 27 Aug 2025
Viewed by 482
Abstract
The deployment of large language models (LLMs) in closed-network industrial environments remains constrained by privacy and connectivity limitations. This study presents a retrieval-augmented question-answering system designed to operate entirely offline, integrating local vector embeddings, ontology-based semantic enrichment, and quantized LLMs, while ensuring compliance [...] Read more.
The deployment of large language models (LLMs) in closed-network industrial environments remains constrained by privacy and connectivity limitations. This study presents a retrieval-augmented question-answering system designed to operate entirely offline, integrating local vector embeddings, ontology-based semantic enrichment, and quantized LLMs, while ensuring compliance with industrial security standards like IEC 62351. The system was implemented using OpenChat-3.5 models with two quantization variants (Q5 and Q8), and evaluated through comparative experiments focused on response accuracy, generation speed, and secure document handling. Empirical results show that both quantized models delivered comparable answer quality, with the Q5 variant achieving approximately 1.5 times faster token generation under limited hardware. The ontology-enhanced retriever further improved semantic relevance by incorporating structured domain knowledge into the retrieval stage. Throughout the experiments, the system demonstrated effective performance across speed, accuracy, and information containment—core requirements for AI deployment in security-sensitive domains. These findings underscore the practical viability of offline LLM systems for privacy-compliant document search, while also highlighting architectural considerations essential for extending their utility to environments such as smart grids or defense-critical infrastructures. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Power Electronics and Smart Grids: Enhancing Efficiency, Reliability and Sustainability)
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14 pages, 7523 KB  
Article
Integrated Junction Barrier Schottky Diode and MOS-Channel Diode in SiC Planar MOSFETs for Optimization of Reverse Performances
by Xinyu Li, Feng He, Xiping Niu, Ling Sang, Yawei He, Kaixuan Xu, Yan Tian, Xintian Zhou, Yunpeng Jia and Rui Jin
Electronics 2024, 13(23), 4770; https://doi.org/10.3390/electronics13234770 - 2 Dec 2024
Viewed by 1131
Abstract
A novel planar silicon carbide (SiC) MOSFET integrated with both MOS-channel diode (MCD) and junction barrier Schottky diode (JBS) on the same chip (MCD-JBSFET) is proposed and investigated through Technology Computer-Aided Design (TCAD) simulations in this paper. The proposed device features the lowest [...] Read more.
A novel planar silicon carbide (SiC) MOSFET integrated with both MOS-channel diode (MCD) and junction barrier Schottky diode (JBS) on the same chip (MCD-JBSFET) is proposed and investigated through Technology Computer-Aided Design (TCAD) simulations in this paper. The proposed device features the lowest turn-on voltage and the best current conduction capability under the reverse-biased conditions, allowing it to achieve the same reverse conduction capability with fewer MCDs compared to conventional MOSFET with MCD structures (MCDFET). This reduction in the number of MCDs enables more channels to operate under forward-biased conditions, thereby improving power density. Compared to a conventional MOSFET integrated with JBS structure (JBSFET), the reverse current in the MCD-JBSFET flows through both the MCD and JBS, which suppresses the peak lattice temperature at Schottky contact and enhances the high-temperature robustness, especially under surge current conditions. In addition, the split-gate structure in the proposed structure optimizes the reverse capacitance and the figure of merit Ron,sp × Qg by factors of 0.65 and 2.15, respectively. Finally, the switching losses are reduced by 40.2%, indicating the suitability of MCD-JBSFET for high-frequency and high-current applications. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Power Electronics and Smart Grids: Enhancing Efficiency, Reliability and Sustainability)
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Review

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63 pages, 4571 KB  
Review
From Design to Deployment: A Comprehensive Review of Theoretical and Experimental Studies of Multi-Energy Systems for Residential Applications
by Taimoor Ahmad Khan, Fadi Kahwash, Jubaer Ahmed, Keng Goh and Savvas Papadopoulos
Electronics 2025, 14(11), 2221; https://doi.org/10.3390/electronics14112221 - 29 May 2025
Cited by 3 | Viewed by 1349
Abstract
Multi-energy systems (MESs) use more than one energy vector to fulfil users’ electrical, thermal, and cooling demands. This paper examines the recent developments in the design, optimisation, and implementation of MESs, focusing on residential applications. Firstly, recent advances in the design and optimisation [...] Read more.
Multi-energy systems (MESs) use more than one energy vector to fulfil users’ electrical, thermal, and cooling demands. This paper examines the recent developments in the design, optimisation, and implementation of MESs, focusing on residential applications. Firstly, recent advances in the design and optimisation of MESs are explained and analysed. The field is characterised by the proliferation of bespoke optimisation methods suitable for this kind of problem. Secondly, practical implementation in the laboratory of MESs and microgrids supplying electrical and thermal loads is discussed. The hardware requirements, in terms of controllers and converters, are critically analysed. This is contrasted with the real-world implementation of MESs or multi-output microgrids in the real world. A description of the communication infrastructure required for real-world implementation is discussed. Finally, a critical review of the entire process, the areas of challenge, and potential research opportunities are presented. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Power Electronics and Smart Grids: Enhancing Efficiency, Reliability and Sustainability)
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