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 59
Special Issue Editors
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
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
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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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