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Energies
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Fault Detection and Diagnosis of Power Distribution System
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Fault Detection and Diagnosis of Power Distribution System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2445

Special Issue Editors

Dr. Wenhai Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering, Sichuan University, Chengdu, China
Interests: distribution network fault detection and early warning; power disturbance data analysis; transmission and transformation equipment status monitoring
Dr. Xueneng Su

E-Mail Website
Guest Editor
State Grid Sichuan Power Supply Company Electric Science Research Institute, Chengdu, China
Interests: distribution system automation; intelligient distribution system
Dr. Shu Zhang

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu, China
Interests: distribution network protection and fault location; power load characteristics and modeling; power disturbance analysis

Special Issue Information

Dear Colleagues,

Distribution systems are very important for achieving reliable and safe power supply. There have been multiple serious explosion and fire accidents caused by distribution system faults. These faults were inevitable due to operational problems, poor maintenance, issues with insulation, and many other challenges. The traditional issues with distribution systems have not been solved yet, including accurate fault location and high-impedance fault detection. These are still tough and challenging problem for the power utilities. Moreover, new problems have emerged due to the large number of new energy production, energy storage, and electric vehicle-charging facilities connected to the distribution system, enhancing the complexity and diversity of fault features.

This Special Issue aims to present and disseminate the most recent advances related to high-impedance fault detection, fault analysis, fault diagnosis, fault location, fault anticipation, protection, electric fires, and the condition monitoring of power distribution systems.

Topics of interest for publication include, but are not limited to, the following areas:

  1. Fault detection and location;
  2. Fault type and fault cause identification;
  3. High-impedance fault detection;
  4. Incipient fault detection and fault anticipation;
  5. Electric fire detection and warning;
  6. Distribution system protection;
  7. Distribution system equipment condition monitoring.

Dr. Wenhai Zhang
Dr. Xueneng Su
Dr. Shu Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • distribution system
  • fault detection
  • fault diagnosis
  • fault anticipation

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Published Papers (3 papers)

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Research

26 pages, 5877 KB  
Article
Generalized Lissajous Trajectory Image Learning for Multi-Load Series Arc Fault Detection in 220 V AC Systems Considering PV and Battery Storage
by Wenhai Zhang, Rui Tang, Junjian Wu, Yiwei Chen, Chunlan Yang and Shu Zhang
Energies 2025, 18(22), 5916; https://doi.org/10.3390/en18225916 - 10 Nov 2025
Viewed by 372
Abstract
This paper proposes a novel AC side series arc fault (SAF) identification method based on Generalized Lissajous Trajectory (GLT) learning for low-voltage residential circuits. The method addresses challenges in detecting SAFs—characterized by high concealment, random occurrence, and limitations in existing protection devices—by leveraging [...] Read more.
This paper proposes a novel AC side series arc fault (SAF) identification method based on Generalized Lissajous Trajectory (GLT) learning for low-voltage residential circuits. The method addresses challenges in detecting SAFs—characterized by high concealment, random occurrence, and limitations in existing protection devices—by leveraging the Hilbert transform to map current signals into 2D Generalized Lissajous Trajectories. These trajectories amplify key SAF features (e.g., zero-break distortion and random pulses). A ResNet50-based image recognition model achieves high-precision fault detection under specific load types, with a validation accuracy of up to 99.91% for linear loads and 98.93% for nonlinear loads. The algorithm operates within 1.6 ms, enabling real-time circuit breaker tripping. The proposed method achieves higher recognition accuracy with lower computational cost compared to other image-based methods. In this paper, an adjustable load signal modeling approach is proposed to visualize the current signal using GLT and complete the lightweight identification based on ResNet network, which provides new ideas and methods for series arc fault detection. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Power Distribution System)
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29 pages, 1454 KB  
Article
A Substation Image Inspection Method Based on Visual Communication and Combination of Normal and Abnormal Samples
by Donglai Tang, Zhongyuan Fan, Youbo Liu and Xiang Wan
Energies 2025, 18(17), 4700; https://doi.org/10.3390/en18174700 - 4 Sep 2025
Viewed by 968
Abstract
To address the issue of missed detection of abnormal images caused by scarcity of defect samples and inadequate model training that characterize the current substation image inspection methods, this paper proposes a new substation image inspection method based on visual communication and combination [...] Read more.
To address the issue of missed detection of abnormal images caused by scarcity of defect samples and inadequate model training that characterize the current substation image inspection methods, this paper proposes a new substation image inspection method based on visual communication and combination of normal and abnormal samples. In this new method, the quality of substation equipment images is first evaluated, and images are recaptured when they are defocused and underexposed. Images are then preprocessed to eliminate the impact of noise on the algorithm. Image feature alignment is then performed to mitigate camera displacement errors that could degrade algorithmic accuracy. Subsequently, normal-labeled images are used to train the model, and a normal sample database is thus established. Built upon visual communication infrastructure with low-level quantization, the visual feature discrepancy between the current inspection images and those in the normal sample database is calculated using the Learned Perceptual Image Patch Similarity (LPIPS) metric. Through this process, the normal images are filtered out while abnormal images are classified and reported. Finally, this new method is validated at a municipal power supply company in China. When the abnormal image reporting rate is 18.9%, the abnormal image reporting accuracy rate is 100%. This demonstrates that the proposed method can significantly decrease the workload of substation operation and maintenance personnel in reviewing substation inspection images, reduce the time required for a single inspection of substation equipment, and improve the efficiency of video-based substation inspections. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Power Distribution System)
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19 pages, 3770 KB  
Article
Segmentation of 220 kV Cable Insulation Layers Using WGAN-GP-Based Data Augmentation and the TransUNet Model
by Liang Luo, Song Qing, Yingjie Liu, Guoyuan Lu, Ziying Zhang, Yuhang Xia, Yi Ao, Fanbo Wei and Xingang Chen
Energies 2025, 18(17), 4667; https://doi.org/10.3390/en18174667 - 2 Sep 2025
Viewed by 799
Abstract
This study presents a segmentation framework for images of 220 kV cable insulation that addresses sample scarcity and blurred boundaries. The framework integrates data augmentation using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and the TransUNet architecture. Considering the difficulty and [...] Read more.
This study presents a segmentation framework for images of 220 kV cable insulation that addresses sample scarcity and blurred boundaries. The framework integrates data augmentation using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and the TransUNet architecture. Considering the difficulty and high cost of obtaining real cable images, WGAN-GP generates high-quality synthetic data to expand the dataset and improve the model’s generalization. The TransUNet network, designed to handle the structural complexity and indistinct edge features of insulation layers, combines the local feature extraction capability of convolutional neural networks (CNNs) with the global context modeling strength of Transformers. This combination enables accurate delineation of the insulation regions. The experimental results show that the proposed method achieves mDice, mIoU, MP, and mRecall scores of 0.9835, 0.9677, 0.9840, and 0.9831, respectively, with improvements of approximately 2.03%, 3.05%, 2.08%, and 1.98% over a UNet baseline. Overall, the proposed approach outperforms UNet, Swin-UNet, and Attention-UNet, confirming its effectiveness in delineating 220 kV cable insulation layers under complex structural and data-limited conditions. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Power Distribution System)
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