Advances in Fault Diagnosis Methods of Power Systems and Key Components

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

Deadline for manuscript submissions: 15 August 2026 | Viewed by 674

Special Issue Editors


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Guest Editor
College of Engineering and Technology, Southwest University, Chongqing 400716, China
Interests: pulsed power technology and its application; condition monitoring; fault diagnosing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. School of Electrical Engineering, Hebei University of Technology, Tianjing 300401, China
2. State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjing 300401, China
Interests: electrical equipment intelligent diagnosis and health management; high-reliability pulsed power technology; power equipment life-cycle management; advanced detection technology development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous expansion of the scale of power systems and the large-scale integration of renewable energy, the safe and stable operation of power systems and equipment faces increasingly severe challenges. Power systems and key components are prone to various faults, such as short circuits, discharges, overheating, mechanical failures, insulation faults, and moisture damage, which seriously affect the stability of the system. Therefore, it is crucial to detect and evaluate those faults. As a key technology to ensure the reliability of power systems and enhance the efficiency of the operation and maintenance of key components, fault diagnosis has made significant progress in terms of its theoretical methods, data-driven technologies, and intelligent applications in recent years.

This Special Issue aims to showcase the latest research and state-of-the-art contributions in fault diagnosis, condition monitoring, and health assessment for power systems and key components. It will aim to provide solutions for the difficulties faced in safety assurance, and also aims to yield insights related to the efficiency, security, reliability, and sustainability of power systems and equipment.

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

  • Condition monitoring of power systems;
  • Fault diagnosis of transformers, machines, reactors, cables, etc.;
  • Innovative applications of technologies such as signal processing and image recognition;
  • Multi-source data fusion and intelligent analysis;
  • New fault diagnosis methods based on machine learning and deep learning.

We look forward to receiving your contributions.

Dr. Zhongyong Zhao
Dr. Xiaozhen Zhao
Guest Editors

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Keywords

  • fault diagnosis
  • fault detection
  • condition monitoring
  • power system
  • power equipment
  • application of artificial intelligence
  • deep learning
  • insulation fault
  • mechanical fault

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Published Papers (2 papers)

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Research

19 pages, 3326 KB  
Article
Pattern Recognition of GIS Partial Discharge Based on UHF Signal Characteristics
by Shaoming Pan, Wei Zhang, Yuan Ma, Yi Su and Wei Huang
Electronics 2026, 15(5), 1096; https://doi.org/10.3390/electronics15051096 - 6 Mar 2026
Viewed by 199
Abstract
The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial [...] Read more.
The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article
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23 pages, 12547 KB  
Article
Data-Efficient Insulator Defect Detection in Power Transmission Systems via Multi-Granularity Feature Learning and Latent Context-Aware Fusion
by Xingxing Fan, Manxiang Gao, Yong Wang, Haining Tang, Fengyong Sun and Changpo Song
Electronics 2026, 15(5), 1081; https://doi.org/10.3390/electronics15051081 - 5 Mar 2026
Viewed by 265
Abstract
Real-world power transmission inspection faces acute data scarcity and severe class imbalance, as defective insulator instances are exceptionally rare compared to normal samples. To enable robust defect detection under such constraints, we present MS-LaT—a backbone networkthat fuses multi-granularity feature learning with latent context-aware [...] Read more.
Real-world power transmission inspection faces acute data scarcity and severe class imbalance, as defective insulator instances are exceptionally rare compared to normal samples. To enable robust defect detection under such constraints, we present MS-LaT—a backbone networkthat fuses multi-granularity feature learning with latent context-aware fusion. The architecture processes visual inputs through a streamlined pipeline: an input stage employing AdaptTeLU-augmented inverted multi-scale separable-residual convolutions to discern subtle local anomalies; a contextual reasoning stage powered by a Latent Transformer encoder with Multi-Head Latent Attention (MLA) for holistic scene understanding; and an output stage utilizing AdaptTeLU-refined inverted multi-scale convolutions to produce precise diagnostic decisions. Domain-adaptive batch normalization (AdaBN) is embedded to minimize cross-domain feature divergence, substantially boosting generalization across diverse operational environments. Research utilising real-world engineering datasets demonstrates the proposed method’s robust insulator defect detection capability in complex environments. Full article
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