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Electronics
Special Issues
Applications of Artificial Intelligence in Electric Power Systems
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Applications of Artificial Intelligence in Electric Power Systems

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

Deadline for manuscript submissions: 15 April 2026 | Viewed by 715

Special Issue Editors

Dr. Chaojun Shi

E-Mail Website
Guest Editor
Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China
Interests: new energy power prediction; intelligent operation and maintenance of power equipment; electric power artificial intelligence large model
Dr. Yurong Guo

E-Mail Website
Guest Editor
Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China
Interests: pattern recognition; power vision; computer vision; meta learning; small sample learning; multimodal fusion
Dr. Shuoshi Li

E-Mail Website
Guest Editor
Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China
Interests: deep learning; image enhancement; power vision; large models

Special Issue Information

Dear Colleagues,

The global energy transition has entered a critical period, driven by the "dual carbon" goals, and the demand for clean and intelligent new power systems is becoming increasingly urgent. Artificial intelligence technology, with its powerful data processing capabilities, has become a key technology for solving complex problems in the field of energy and power. From accurate prediction of new energy generation to intelligent dispatching of the power grid and from intelligent operation and maintenance of power equipment to intelligent energy use on the user side, artificial intelligence technology is deeply integrated into the entire energy production, transmission, and consumption chain, promoting the accelerated evolution of traditional power systems to new power systems.

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

  • Electric power artificial intelligence;
  • Electric power computer vision;
  • Defect detection of transmission equipment;
  • Defect detection of substation equipment;
  • Defect detection of photovoltaic panels;
  • Fault diagnosis of fan blades;
  • Photovoltaic power prediction;
  • Wind power prediction;
  • Power load forecasting;
  • Power Internet of Things technology.

We look forward to receiving your contributions.

Dr. Chaojun Shi
Dr. Yurong Guo
Dr. Shuoshi Li
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. 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

  • electric power artificial intelligence
  • defect detection of power equipment
  • new energy power prediction
  • power internet of things technology

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Published Papers (2 papers)

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Research

17 pages, 14984 KB  
Article
Substation Instrument Defect Detection Based on Multi-Domain Collaborative Attention Fusion
by Kequan Liu, Yandong Li, Shiwei Wang, Zhaoguang Yang, Zhixin Li and Zhenbing Zhao
Electronics 2025, 14(23), 4690; https://doi.org/10.3390/electronics14234690 - 28 Nov 2025
Viewed by 224
Abstract
The detection of defects in substation instruments, such as surge arrester counters, is hindered by subtle characteristic changes and severe class imbalance. To address these challenges, this study proposes an enhanced detection algorithm based on multi-domain collaborative attention fusion (MDCAF). This method integrates [...] Read more.
The detection of defects in substation instruments, such as surge arrester counters, is hindered by subtle characteristic changes and severe class imbalance. To address these challenges, this study proposes an enhanced detection algorithm based on multi-domain collaborative attention fusion (MDCAF). This method integrates three key contributions: hybrid enhancement to alleviate boundary blurring in transition samples; the MDCAF module, which collaboratively captures features across channel, space, and axis domains; and a class-weight balancing strategy to optimize learning for rare defects. The experimental results show that the average precision (mAP) is 90.1%, which is 2.8 percentage points higher than the baseline, reducing both missed and false detections. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Electric Power Systems)
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16 pages, 2083 KB  
Article
A Corrosion Segmentation Method for Substation Equipment Based on Improved TransU-Net and Multimodal Feature Fusion
by Hailong Guo, Guangqi Lu, Jiuyu Guo, Zhixin Li, Xuan Wang and Zhenbing Zhao
Electronics 2025, 14(23), 4688; https://doi.org/10.3390/electronics14234688 - 28 Nov 2025
Viewed by 199
Abstract
Substation equipment operating in harsh environments is highly susceptible to corrosion, yet conventional image segmentation methods often fail to achieve precise delineation of corroded regions. Here, we propose an enhanced TransU-Net-based approach for corrosion segmentation. Deformable convolution is incorporated into the encoder to [...] Read more.
Substation equipment operating in harsh environments is highly susceptible to corrosion, yet conventional image segmentation methods often fail to achieve precise delineation of corroded regions. Here, we propose an enhanced TransU-Net-based approach for corrosion segmentation. Deformable convolution is incorporated into the encoder to strengthen the model’s capacity to represent irregular corrosion morphologies. A composite color–texture fusion module is developed to jointly exploit color information from HSV and Lab spaces together with multi-scale texture features. In addition, a Shape-IoU loss function is introduced to refine boundary fitting and improve contour accuracy. Experimental evaluations demonstrate that the proposed method consistently outperforms state-of-the-art models across multiple metrics, achieving an Intersection over Union (IoU) of 75.42% and a Recall (PA) of 83.14%. These results confirm that the model substantially enhances corrosion recognition accuracy and edge integrity under complex background conditions, offering a promising strategy for intelligent maintenance of substation infrastructure. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Electric Power Systems)
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