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Emerging Techniques Towards Safety Assurance and Reliability Design in Electrical...
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Emerging Techniques Towards Safety Assurance and Reliability Design in Electrical Assets

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1025

Special Issue Editors

Dr. Huamin Jie

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: electromagnetic interference; impedance modeling; fault diagnosis; electromagnetic safety and security
Dr. Yuchen He

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310, USA
Interests: modeling and control; stability and reliability
Dr. Hanchi Zhang

E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Interests: lightning protection; insulation coordination; power system transients; multi-carrier energy system

Special Issue Information

Dear Colleagues,

In the era of electrification and intelligent systems, a wider array of electrical and electronic assets are being employed across various sectors, including urban mobility, smart grids, industrial automation, civilian infrastructure, etc. Ensuring the safety of these assets is of paramount importance, which not only prevents unexpected service interruptions but also safeguards the well-being of both individuals and equipment. Furthermore, their reliability is directly tied to a system’s inherent stability and its capability to withstand external interference, serving as a key indicator in the design and standardization process. Given the advancements in electrical technology and the continuously growing demand for electricity, novel techniques regarding safety and reliability are ongoing research spotlights and are expected to garner more attention in the future. Therefore, this Special Issue is dedicated to providing a platform for showcasing the latest studies about safety and reliability for electrical devices and assets, fostering interdisciplinary discussions, and spurring the progress of future industries.

In this Special Issue, original research articles and reviews are welcome. Relevant topics include, but are not limited to, the following:

  • Electromagnetic compatibility, environment, and interference;
  • Risk assessment and management in electrical and electronic system;
  • Advanced control strategy against internal and external disturbance;
  • Intentional attack and countermeasures;
  • Artificial intelligence for safety and reliability design;
  • Characterization and modeling of electrical components, devices and systems;
  • Direct and indirect lightning protection strategies;
  • Insulation coordination for internal overvoltage issues;
  • Power electronics protection apparatus and its maintenance;
  • Digital twin system for system diagnosis and prognosis.

We look forward to receiving your contributions.

Dr. Huamin Jie
Dr. Yuchen He
Dr. Hanchi Zhang
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

  • safety
  • reliability
  • electromagnetic compatibility
  • system stability
  • system diagnosis and prognosis
  • lighting protection
  • protection and maintenance in power electronics
  • artificial intelligence
  • overvoltage issues

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Published Papers (2 papers)

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19 pages, 2241 KiB  
Article
OR-MTL: A Robust Ordinal Regression Multi-Task Learning Framework for Partial Discharge Diagnosis in Gas-Insulated Switchgear
by Jifu Li, Jianyan Tian and Gang Li
Electronics 2025, 14(7), 1262; https://doi.org/10.3390/electronics14071262 - 23 Mar 2025
Viewed by 162
Abstract
This paper proposes a novel Ordinal Regression Multi-Task Learning (OR-MTL) framework to address challenges in multi-task diagnosis of PD in Gas-Insulated Switchgear (GIS). GIS PD diagnosis typically involves tasks such as discharge-type identification and severity assessment, which is essentially an ordinal regression problem [...] Read more.
This paper proposes a novel Ordinal Regression Multi-Task Learning (OR-MTL) framework to address challenges in multi-task diagnosis of PD in Gas-Insulated Switchgear (GIS). GIS PD diagnosis typically involves tasks such as discharge-type identification and severity assessment, which is essentially an ordinal regression problem facing challenges such as high label noise and inconsistent ranking of prediction outcomes. To address these challenges, the OR-MTL framework introduces two key innovations: a dynamic task-weighting strategy based on excess risk estimation, which mitigates the negative impact of label noise on multi-task learning weight allocation, and an ordinal regression loss function based on conditional probability, which ensures consistent prediction ranking through conditional probability chains. Experiments on GIS PD datasets demonstrate that the excess risk-based task-weighting strategy exhibits superior robustness compared to traditional methods in high-noise environments, while the proposed ranking consistency loss function significantly improves the accuracy of severity assessment and reduces errors. Ablation studies further validate the effectiveness of the complete OR-MTL framework. This research not only provides an efficient solution for GIS PD diagnosis but also offers new insights and methodologies for multi-task learning involving ordinal regression tasks. Full article
(This article belongs to the Special Issue Emerging Techniques Towards Safety Assurance and Reliability Design in Electrical Assets)
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17 pages, 4014 KiB  
Article
High-Resistance Grounding Fault Location in High-Voltage Direct Current Transmission Systems Based on Deep Residual Shrinkage Network
by Ping Huang, Junlin Huang, Shengquan Huang, Guoting Yang and Zhipeng Wu
Electronics 2025, 14(3), 628; https://doi.org/10.3390/electronics14030628 - 5 Feb 2025
Viewed by 483
Abstract
Due to the precision limitations of traditional fault location methods in identifying grounding faults in High-Voltage Direct Current (HVDC) transmission systems and considering the high occurrence probability of high-resistance grounding faults in practical engineering scenarios coupled with the sampling accuracy constraints of actual [...] Read more.
Due to the precision limitations of traditional fault location methods in identifying grounding faults in High-Voltage Direct Current (HVDC) transmission systems and considering the high occurrence probability of high-resistance grounding faults in practical engineering scenarios coupled with the sampling accuracy constraints of actual equipment, this article introduces a novel approach for high-resistance grounding fault location in HVDC transmission lines. This method integrates Variational Mode Decomposition (VMD) and Deep Residual Shrinkage Network (DRSN). Initially, VMD is employed to decompose double-ended high-resistance grounding fault signals, extracting the corresponding Intrinsic Mode Functions (IMF). These IMF signals are then preprocessed to construct the input data for the DRSN model. Upon training, the model outputs the precise fault location. To validate the effectiveness of the proposed method, a ±800 kV bipolar HVDC transmission system model is established using PSCAD/EMTDC version 4.6.2 software for simulating high-resistance grounding faults. The sampling accuracy of the model’s output signals is set to 10 kHz, aligning closely with actual engineering equipment specifications. Comprehensive simulation experiments and anti-interference analyses are conducted on the DRSN model. The results demonstrate that the fault location method based on the DRSN exhibits high accuracy in locating high-resistance grounding faults, with a maximum error of less than 1.5 km, even when considering factors such as engineering sampling frequency, fault types, and signal noise. Full article
(This article belongs to the Special Issue Emerging Techniques Towards Safety Assurance and Reliability Design in Electrical Assets)
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