Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.2
Journals
Energies
Special Issues
Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and...
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 699

Special Issue Editors

Dr. Fuqiang Ren

E-Mail Website
Guest Editor
Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment, Shandong University, Jinan 250100, China
Interests: multi-field coupling analysis of power equipment; reliability improvement of power equipment; lifespan prediction; condition assessment; fault diagnosis of power equipment; digital twin technology of power equipment
Prof. Dr. Qingquan Li

E-Mail Website
Guest Editor
Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment, Shandong University, Jinan 250100, China
Interests: discharge characteristics of composite insulation of high-voltage equipment; insulation degradation mechanism; insulation status assessment; application of artificial intelligence in power equipment

Special Issue Information

Dear Colleagues,

The reliability and operational efficiency of transformers—spanning power transformers, converter transformers, and high-frequency transformers—are pivotal to the stability of modern electrical grids, renewable energy integration, and advanced power electronic systems. As these critical components face escalating demands from aging infrastructure, dynamic load conditions, and the transition to smart grids and electrified transportation, precise fault diagnosis, robust simulation methodologies, and adaptive maintenance frameworks have become imperative. Challenges such as insulation degradation, thermal stress, extreme operating environments, and high-power-density requirements further underscore the urgency for innovative solutions to enhance transformer resilience and longevity.

This Special Issue seeks to compile cutting-edge research and practical advancements in fault detection, diagnostic techniques, and simulation-driven approaches tailored to power transformers, converter transformers, and high-frequency transformers. Topics of interest include, but are not limited to:

  • Advanced fault detection and localization methods for transformers in grid and power electronic applications.
  • Physics-based and data-driven simulation models for thermal, electrical, and mechanical behavior analysis.
  • Artificial intelligence (AI)/machine learning (ML)-driven prognostic frameworks for insulation aging, partial discharge, and winding deformation.
  • High-frequency transformer modeling for wide-bandgap semiconductor applications and renewable energy systems.
  • Condition monitoring techniques integrating IoT, edge computing, and real-time sensor networks.
  • Reliability assessment, failure mode analysis, and life prediction techniques under extreme operating conditions (e.g., overload, harmonics).
  • Digital twin development for predictive maintenance of converter transformers in HVDC and FACTS systems.
  • Comparative studies of diagnostic tools and modeling techniques for power, converter, and high-frequency transformers.
  • Case studies on industrial, renewable energy, and transportation fault mitigation.

We encourage submissions of original research, reviews, and case studies demonstrating transformative approaches to transformer health management. Contributions highlighting scalable solutions, hybrid simulation-experimental validation, and interoperability with smart grid architectures are particularly welcome. This Special Issue aspires to advance the state of the art in transformer diagnostics and simulation, ultimately supporting the development of safer, more efficient, and sustainable electrical systems worldwide.

Dr. Fuqiang Ren
Prof. Dr. Qingquan Li
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. 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

  • fault diagnosis
  • simulation models
  • condition monitoring
  • reliability assessment
  • power transformers
  • converter transformers
  • high-frequency transformers
  • predictive maintenance
  • artificial intelligence (AI)
  • machine learning (ML)
  • digital twin
  • Internet of Things (IoT)
  • partial discharge
  • insulation aging
  • winding deformation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 3043 KiB  
Article
Transformer Oil Acid Value Prediction Method Based on Infrared Spectroscopy and Deep Neural Network
by Linjie Fang, Chuanshuai Zong, Zhenguo Pang, Ye Tian, Xuezeng Huang, Yining Zhang, Xiaolong Wang and Shiji Zhang
Energies 2025, 18(13), 3345; https://doi.org/10.3390/en18133345 - 26 Jun 2025
Viewed by 121
Abstract
The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. [...] Read more.
The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. Early detection of rising acid levels is critical to prevent transformer insulation degradation, corrosion, and failure. Conversely, delayed detection accelerates aging and can cause costly repairs or unplanned outages. To address this need, this paper proposes a new method for predicting the acid value content of the transformer oil based on the infrared spectra in the transformer oil and a deep neural network (DNN). The infrared spectral data of the transformer oil is acquired by ALPHA II FT-IR spectrometer, the high frequency noise effect of the spectrum is reduced by wavelet packet decomposition (WPD), and the bootstrapping soft shrinkage (BOSS) algorithm is used to extract the spectra with the highest correlation with the acid value content. The BOSS algorithm is used to extract the feature parameters with the highest correlation with the acid value content in the spectrum, and the DNN prediction model is established to realize the fast prediction of the acid value content of the transformer oil. In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R2) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. In addition, the accuracy is 5.45% higher than the traditional model, and the R2 of the proposed prediction model is 95.04% after complete external data validation, indicating that it has good accuracy. The results show that the infrared spectral analysis method combining WPD noise reduction, BOSS feature extraction, and DNN modeling can realize the rapid prediction of the acid value content of the transformer oil based on infrared spectroscopy technology, and the prediction model can be used to realize the analytical study of transformer oils. The model can be further applied to the monitoring field of the transformer oil characteristic parameter to realize the rapid monitoring of the transformer oil parameters based on a portable infrared spectrometer. Full article
(This article belongs to the Special Issue Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers)
►▼ Show Figures

Figure 1

21 pages, 6269 KiB  
Article
Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network
by Shan Wang, Zhihu Hong, Qingyun Min, Dexu Zou, Yanlin Zhao, Runze Qi and Tong Zhao
Energies 2025, 18(11), 2934; https://doi.org/10.3390/en18112934 - 3 Jun 2025
Viewed by 308
Abstract
Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper develops a hybrid framework combining metaheuristic-optimized decomposition with hierarchical temporal learning. The methodology [...] Read more.
Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper develops a hybrid framework combining metaheuristic-optimized decomposition with hierarchical temporal learning. The methodology employs a Subtraction-Average-Based Optimizer (SABO) to adaptively configure Time-Varying Filtered Empirical Mode Decomposition (TVFEMD), effectively resolving mode mixing through optimized parameter selection. The decomposed components undergo dual-stage temporal processing: A Temporal Convolutional Network (TCN) extracts multi-scale dependencies via dilated convolution architecture, followed by Gated Recurrent Unit (GRU) layers capturing dynamic temporal patterns. An experimental platform was established using a KM-type OLTC to acquire vibration signals under typical mechanical faults, subsequently constructing the dataset. Experimental validation demonstrates superior classification accuracy compared to conventional decomposition–classification approaches in distinguishing complex mechanical anomalies, achieving a classification accuracy of 96.38%. The framework achieves significant accuracy improvement over baseline methods while maintaining computational efficiency, validated through comprehensive mechanical fault simulations. This parameter-adaptive methodology demonstrates enhanced stability in signal decomposition and improved temporal feature discernment, proving particularly effective in handling non-stationary vibration signals under real operational conditions. The results establish practical viability for industrial condition monitoring applications through robust feature extraction and reliable fault pattern recognition. Full article
(This article belongs to the Special Issue Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop