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Recent Advances in Electrical Engineering: Emerging Research and Perspectives
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Recent Advances in Electrical Engineering: Emerging Research and Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1893

Special Issue Editors

Prof. Dr. Jingang Wang

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chonqing 400044, China
Interests: electromagnetic calculation; electrical equipment; measurement
Special Issues, Collections and Topics in MDPI journals
Dr. Pengcheng Zhao

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chonqing 400044, China
Interests: energy systems; sensors; electromagnetic measurement; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of electrical engineering is experiencing rapid advancements driven by innovations in technology, materials, and methodologies. This Special Issue seeks to highlight the latest developments that are shaping the future of the discipline.

The aim is to bring together cutting-edge research that spans a wide range of topics, including but not limited to renewable energy, signal processing, telecommunications, control systems, and electromagnetics. This collection of articles showcases groundbreaking work from leading researchers and provides insights into emerging trends and future directions in electrical engineering. The selected papers will not only present novel theoretical contributions but also emphasize practical applications and experimental validations. By focusing on both current challenges and upcoming opportunities, this Special Issue intends to serve as a valuable resource for researchers, engineers, and practitioners.

Prof. Dr. Jingang Wang
Dr. Pengcheng Zhao
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. Applied Sciences 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

  • electrical engineering
  • renewable energy
  • signal processing
  • telecommunications
  • control systems
  • networked systems
  • electromagnetics
  • bioengineering
  • emerging technologies
  • advanced materials
  • experimental validation
  • theoretical contributions
  • artificial intelligence (AI)
  • applied engineering solutions
  • engineering applications

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Published Papers (3 papers)

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15 pages, 2568 KiB  
Article
Stable Variable Fixation for Accelerated Unit Commitment via Graph Neural Network and Linear Programming Hybrid Learning
by Linfeng Yang, Peilun Li, Shifei Chen and Haiyan Zheng
Appl. Sci. 2025, 15(8), 4498; https://doi.org/10.3390/app15084498 - 18 Apr 2025
Viewed by 221
Abstract
The Unit Commitment Problem (UCP) is a critical component of power market decision-making and is typically formulated as Mixed Integer Programming (MIP). Given the complexity of solving MIPs, efficiently solving large-scale UCPs remains a significant challenge. This paper presents a hybrid Graph Neural [...] Read more.
The Unit Commitment Problem (UCP) is a critical component of power market decision-making and is typically formulated as Mixed Integer Programming (MIP). Given the complexity of solving MIPs, efficiently solving large-scale UCPs remains a significant challenge. This paper presents a hybrid Graph Neural Network (GNN)–Linear Programming (LP) framework to accelerate the solution of large-scale Unit Commitment Problems (UCPs) while maintaining the quality of solutions. By analyzing variable stability through historical branch-and-bound (B&B) trajectories, we classify MIP variables into dynamically adjustable stable and unstable groups. We adopt an MIP formulation that includes multiple types of binary variables—such as commitment, startup, and shutdown variables—and extract additional information from these auxiliary binary variables. This enriched representation provides more candidates for stable variable fixation, helping to improve variable refinement, mitigate suboptimality, and enhance computational efficiency. A bipartite GNN is trained offline to predict stable variables based on system topology and historical operational patterns. During online optimization, instance-specific root LP solutions refine these predictions, enabling adaptive variable fixation via a dual-threshold mechanism that integrates GNN confidence and LP relaxations. To mitigate suboptimality risks, we introduce temporally flexible fixation strategies—hard fixation for variables with persistent stability and soft fixation allowing limited temporal adjustments—alongside a GNN-guided branching rule to prioritize unstable variables. Numerical experiments demonstrate that jointly fixing commitment, startup, and shutdown variables yields better performance compared to fixing only commitment variables. Ablation studies further validate the importance of hard fixation and customized branching strategies, especially for large-scale systems. Full article
(This article belongs to the Special Issue Recent Advances in Electrical Engineering: Emerging Research and Perspectives)
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22 pages, 987 KiB  
Article
Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
by Chi Zhang, Zhijun Qin and Yan Sun
Appl. Sci. 2025, 15(6), 3366; https://doi.org/10.3390/app15063366 - 19 Mar 2025
Viewed by 246
Abstract
Unit commitment (UC) is a critical challenge in power system optimization, typically formulated as a high-dimensional mixed-integer linear programming (MILP) problem with non-deterministic polynomial-time hard (NP-hard) complexity. While the branch-and-bound (B&B) algorithm can determine optimal solutions, its computational cost increases exponentially with the [...] Read more.
Unit commitment (UC) is a critical challenge in power system optimization, typically formulated as a high-dimensional mixed-integer linear programming (MILP) problem with non-deterministic polynomial-time hard (NP-hard) complexity. While the branch-and-bound (B&B) algorithm can determine optimal solutions, its computational cost increases exponentially with the number of units, which limits the practical application of UC. Machine learning (ML) has recently emerged as a promising tool for addressing UC, but its effectiveness relies on substantial training samples. Moreover, ML models suffer significant performance degradation when the number of units changes, a phenomenon known as the task mismatch problem. This paper introduces a novel method for Branching Acceleration for UC, aiming to reduce the computational complexity of the B&B algorithm while achieving near-optimal solutions. The method leverages invariant branching tree-related features and UC domain-specific features, employing imitation learning to develop an enhanced pruning policy for more precise node pruning. Numerical studies on both standard and practical testing systems demonstrate that the method significantly accelerates computation with few training samples and negligible accuracy loss. Furthermore, it exhibits robust generalization capability for handling task mismatches and can be seamlessly integrated with other B&B acceleration techniques, providing a practical and efficient solution for UC problems. Full article
(This article belongs to the Special Issue Recent Advances in Electrical Engineering: Emerging Research and Perspectives)
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22 pages, 5859 KiB  
Article
Research on a Wind-Energy-Harvesting Device Based on a Non-Contact Electret–Piezoelectric Coupling Structure
by Qian Wang, Jiankang Bao, Haitao Wu, Jingang Wang, Pengcheng Zhao and Changli Yu
Appl. Sci. 2025, 15(4), 1919; https://doi.org/10.3390/app15041919 - 12 Feb 2025
Viewed by 686
Abstract
Persistently and reliably harvesting wind energy to power intelligent online monitoring devices for transmission lines promotes the intelligent and sustainable development of the Internet of Things. Current small-scale wind-energy-harvesting devices, relying on a single energy conversion principle, face challenges such as low efficiency [...] Read more.
Persistently and reliably harvesting wind energy to power intelligent online monitoring devices for transmission lines promotes the intelligent and sustainable development of the Internet of Things. Current small-scale wind-energy-harvesting devices, relying on a single energy conversion principle, face challenges such as low efficiency and poor performance at low wind speeds. This paper presents a coaxial rotating non-contact coupling transducer structure, and its optimization methods have been studied, which are based on electret electrostatic induction and magnetically actuated piezoelectric conversion. By analyzing the principles of alternating positive–negative unipolar electret components and constructing a finite element model, improved output capacity is demonstrated. The electric signals from electret components are more suitable for inferring the shaft and wind speeds compared to piezoelectric components. The piezoelectric components utilize frequency up-conversion theory to enhance output while addressing the low power density of the electrostatic components. Experimental results indicate that the proposed structure operates reliably at rotational speeds of 100–700 rpm, achieving a maximum output power of 6.742 mW. The output power of the electret electrostatic component’s electrodes nearly doubled, with the signal positively correlated to rotation speed. The optimized structure of the magnetically actuated piezoelectric component achieved a power increase of 11.51% at four excitations and 250 rpm. This study provides a new design approach for more durable and efficient small-scale wind-energy-harvesting devices, as well as for achieving integrated measurement and supply. Full article
(This article belongs to the Special Issue Recent Advances in Electrical Engineering: Emerging Research and Perspectives)
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