Power Equipment Fault Detection and Diagnosis

Dear Colleagues, 

The reliable operation of power equipment is fundamental to ensuring the safety, stability, and efficiency of modern power systems. With the increasing integration of renewable energy, power electronics, and intelligent grid technologies, power equipment is operating under more complex and dynamic conditions, which significantly raises the risk of faults and failures. Therefore, advanced fault detection and diagnosis methods are essential for improving system reliability and reducing maintenance costs.

This Special Issue aims to present recent advances in the fault detection, diagnosis, and condition monitoring of power equipment. It focuses on both theoretical developments and practical applications, including data-driven methods, physics-informed modeling, and intelligent diagnostic techniques under small-sample and cross-domain scenarios.

We are pleased to invite researchers and practitioners to contribute original research articles and review papers addressing emerging challenges and innovative solutions within this field.

Suggested themes and article types for submissions: In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Fault detection and diagnosis of high-voltage equipment (e.g., GIS, transformers, switchgear, electrical machines).
2. Partial discharge analysis and insulation condition assessment.
3. Data-driven and AI-based diagnostic methods (e.g., deep learning, transfer learning, few-shot learning).
4. Physics-informed and hybrid modeling for fault diagnosis.
5. Multi-source data fusion and multi-modal sensing technologies.
6. Cross-domain and cross-scenario generalization methods.
7. Digital twin and intelligent maintenance of power equipment.
8. Signal processing and feature extraction techniques for fault analysis.

We look forward to receiving your contributions.

Dr. Yanxin Wang
Dr. Jing Yan
Guest Editors

Manuscript Submission Information

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