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Electronics
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Deep Learning for Power Transmission and Distribution
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Deep Learning for Power Transmission and Distribution

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1907

Special Issue Editors

Dr. Chathura Wanigasekara

E-Mail Website
Guest Editor
Institute for the Protection of Maritime Infrastructures, German Aerospace Center (DLR), 27572 Bremerhaven, Germany
Interests: nonlinear system identification and control; machine learning; networked control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Akshya Swain

E-Mail Website
Guest Editor
Department of Electrical, Computer & Software Engineering, The University of Auckland, Auckland 1010, New Zealand
Interests: nonlinear system identification; fault diagnosis; fault tolerant control; nonlinear control and nonlinear signal processing; nonlinear control; system identification; bio-inspired computing; machine intelligence; smart grid

Special Issue Information

Dear Colleagues,

The rapid addition of renewable energy sources, rising electricity demands, and the need for improved grid dependability have made it necessity to develop cutting-edge technologies for electrical power systems. The process of power transmission and distribution plays a key role in electrical power systems and has received less attention among researchers. Deep learning is an interesting possible solution which can be used for many applications in power systems.

This Special Issue aims to focus on deep-learning-based techniques to model and resolve issues related to power transmission and distribution. This Special Issue will accept topics regarding deep-learning-based applications in load forecasting, fault detection, and diagnosis; the assessment of the security and stability of power systems; the integration and management of renewable energy sources; and the asset management and maintenance of the electric grid. Other potential topics include:

  • Deep networks for load forecasting;
  • Deep networks for fault detection and diagnosis;
  • Deep networks for the security and stability of power systems;
  • Deep networks for the integration and management of renewable energy sources;
  • Deep networks for asset management;
  • Deep networks for the maintenance of the electric grid.

Dr. Chathura Wanigasekara
Dr. Akshya Swain
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. Electronics 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 2400 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

  • power transmission
  • power distribution
  • deep learning
  • smart grid
  • renewable energy
  • security and stability of power systems
  • fault detection and diagnosis

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Published Papers (2 papers)

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Research

22 pages, 4111 KiB  
Article
Improved YOLO11 Algorithm for Insulator Defect Detection in Power Distribution Lines
by Yanpeng Ji, Da Zhang, Yuling He, Jianli Zhao, Xin Duan and Tuo Zhang
Electronics 2025, 14(6), 1201; https://doi.org/10.3390/electronics14061201 - 19 Mar 2025
Viewed by 796
Abstract
Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line collapse, thus threatening the safe operation of distribution networks. However, [...] Read more.
Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line collapse, thus threatening the safe operation of distribution networks. However, distribution line insulators often present detection challenges due to their compact dimensions, diverse flaw types, and frequent installation in populated areas with visually cluttered environments. The combination of these factors, including small defect sizes, varying failure patterns, and complex background interference, in both urban and rural settings, creates significant difficulties for precise defect identification in these critical components. In response to these challenges, this paper proposes a defect recognition algorithm for distribution line insulators based on the improved YOLO11 model. Firstly, the algorithm combines the detection head of the original model with the Adaptively Spatial Feature Fusion (ASFF) module to effectively fuse defect features at different resolution levels and improve the model’s ability to recognize multi-scale defect features. Secondly, a Bidirectional Feature Pyramid Network (BiFPN) replaces the FPN + PAN structure of the original model to achieve a more effective transfer of contextual information in order to facilitate the model’s efficiency in performing defect feature fusion, and the Convolutional Block Attention Module (CBAM) Attention mechanism is embedded in the BiFPN output so that the model is able to give priority attention to defective features on insulators in complex recognition environments. Finally, the ShuffleNetV2 module is used to reduce the parameters of the improved model by replacing the large-parameter C3k2 module at the end of the backbone network for easy deployment on lightweight and small devices. The experimental results show that the improved model performs well in the distribution line insulator defect detection task, with an accuracy precision (AP) and mean accuracy precision (mAP) of 97.0% and 98.1%, respectively, which are 1.4% and 0.7% higher than the original YOLO11 model. Full article
(This article belongs to the Special Issue Deep Learning for Power Transmission and Distribution)
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19 pages, 8720 KiB  
Article
Spatial Attention-Based Kernel Point Convolution Network for Semantic Segmentation of Transmission Corridor Scenarios in Airborne Laser Scanning Point Clouds
by Fangrong Zhou, Gang Wen, Yi Ma, Hao Pan, Guofang Wang and Yifan Wang
Electronics 2024, 13(22), 4501; https://doi.org/10.3390/electronics13224501 - 15 Nov 2024
Viewed by 768
Abstract
Accurate semantic segmentation in transmission corridor scenes is crucial for the maintenance and inspection of power infrastructure, facilitating the timely detection of potential hazards. In this study, we propose SA-KPConv, an advanced segmentation model specifically designed for transmission corridor scenarios. Traditional approaches, including [...] Read more.
Accurate semantic segmentation in transmission corridor scenes is crucial for the maintenance and inspection of power infrastructure, facilitating the timely detection of potential hazards. In this study, we propose SA-KPConv, an advanced segmentation model specifically designed for transmission corridor scenarios. Traditional approaches, including Random Forest and point-based deep learning models such as PointNet++, demonstrate limitations in segmenting critical infrastructure components, particularly power lines and towers, primarily due to their inadequate capacity to capture complex spatial relationships and local geometric details. Our model effectively addresses these challenges by integrating a spatial attention module with kernel point convolution, enhancing both global context and local feature extraction. Experiments demonstrate that SA-KPConv outperforms state-of-the-art methods, achieving a mean Intersection over Union (mIoU) of 89.62%, particularly excelling in challenging terrains such as mountainous areas. Ablation studies further validate the significance of our model’s components in enhancing overall performance and effectively addressing class imbalance. This study presents a robust solution for semantic segmentation, with considerable potential for monitoring and maintaining power infrastructure. Full article
(This article belongs to the Special Issue Deep Learning for Power Transmission and Distribution)
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