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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 1481

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

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Published Papers (4 papers)

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Research

16 pages, 4730 KiB  
Article
Power Transformer Short-Circuit Force Calculation Using Three and Two-Dimensional Finite-Element Analysis
by Jian Wang, Junchi He, Xiaohan Chen, Tian Tian, Chenguo Yao and Ahmed Abu-Siada
Energies 2025, 18(15), 3898; https://doi.org/10.3390/en18153898 - 22 Jul 2025
Viewed by 190
Abstract
In a power transformer short-circuit, transient current and magnetic flux interactions create strong electromagnetic forces that can deform windings and the core, risking failure. Accurate calculation of these forces during design is critical to prevent such outcomes. This paper employs two-dimensional (2D) and [...] Read more.
In a power transformer short-circuit, transient current and magnetic flux interactions create strong electromagnetic forces that can deform windings and the core, risking failure. Accurate calculation of these forces during design is critical to prevent such outcomes. This paper employs two-dimensional (2D) and three-dimensional (3D) finite-element analysis (FEA) to model a 110 kV, 40 MVA three-phase transformer, calculating magnetic flux density, short-circuit current, and electromagnetic forces. The difference in force values at inner and outer core window positions, reaching up to 40%, is analyzed. The impact of physical winding displacement on axial forces is also studied. Simulation results, validated against analytical calculations, show peak short-circuit currents of 6963 A on the high-voltage (HV) winding and 70,411 A on the low-voltage (LV) winding. Average radial forces were 136 kN on the HV winding and 89 kN on the LV winding, while average axial forces were 8 kN on the HV and 9 kN on the LV. This agreement verifies the FEA models’ reliability. The results provide insights into winding behavior under severe faults and enhance transformer design reliability. Full article
(This article belongs to the Special Issue Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers)
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24 pages, 2267 KiB  
Article
A Mechanical Fault Diagnosis Method for On-Load Tap Changers Based on GOA-Optimized FMD and Transformer
by Ruifeng Wei, Zhenjiang Chen, Qingbo Wang, Yongsheng Duan, Hui Wang, Feiming Jiang, Daoyuan Liu and Xiaolong Wang
Energies 2025, 18(14), 3848; https://doi.org/10.3390/en18143848 - 19 Jul 2025
Viewed by 276
Abstract
Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge [...] Read more.
Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems. Full article
(This article belongs to the Special Issue Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers)
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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 240
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)
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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
Cited by 1 | Viewed by 390
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)
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