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Electronics
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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 1889

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 (5 papers)

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Research

15 pages, 16945 KB  
Article
TLDD-YOLO: An Improved YOLO for Transmission Line Component and Defect Detection
by Kuihao Wang, Yan Huang and Yincheng Qi
Electronics 2026, 15(4), 757; https://doi.org/10.3390/electronics15040757 - 11 Feb 2026
Viewed by 184
Abstract
Unmanned Aerial Vehicle (UAV) inspection of transmission lines faces two primary challenges when detecting and analyzing components or defects in videos or images: poor performance in detecting small objects, and interference from complex backgrounds. To enhance defect detection under such cluttered conditions, this [...] Read more.
Unmanned Aerial Vehicle (UAV) inspection of transmission lines faces two primary challenges when detecting and analyzing components or defects in videos or images: poor performance in detecting small objects, and interference from complex backgrounds. To enhance defect detection under such cluttered conditions, this paper introduces an improved YOLO-based model, termed Transmission Line Defect Detection–YOLO (TLDD-YOLO), which jointly optimizes feature representation via a Dual-Branch Guided Attention (DBGA) mechanism and a Spatial Offset Attention Module (SOAM). DBGA employs a dual-branch structure to extract high-frequency spatial details and channel-wise semantic information, thereby guiding the backbone network to preserve the critical edge and texture features of small objects, mitigating detail loss during downsampling. SOAM utilizes a lightweight offset generation network to produce spatial offset matrices, and dynamically adjusts feature distributions through offset-guided spatial alignment, enabling feature contours to better conform to object shapes while reducing interference from complex backgrounds. The experimental results on a self-constructed transmission line inspection dataset demonstrate that TLDD-YOLO achieves 57.1% mAP, 83.8% mAP50, and 36.1% mAPs. Compared with the baseline model, the proposed method improves mAP, mAP50, and mAPs by 1.8%, 1.8%, and 7.7%, respectively, confirming its effectiveness for small object detection in UAV-based transmission line inspection. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Electric Power Systems)
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35 pages, 8498 KB  
Article
Research on Short-Term Wind Power Forecasting Based on VMD-IDBO-SVM
by Gengda Li, Chaoying Li, Jian Qian, Zilong Ma, Hao Sun, Ridong Jiao, Wei Jia, Yibo Yao and Tiefeng Zhang
Electronics 2026, 15(3), 533; https://doi.org/10.3390/electronics15030533 - 26 Jan 2026
Viewed by 319
Abstract
To enhance the accuracy of wind power forecasting, this paper proposes a hybrid model that integrates Variational Mode Decomposition (VMD), Improved Dung Beetle Optimization (IDBO) and Support Vector Machine (SVM). First, to reduce the volatility and non-stationarity of wind power data, VMD is [...] Read more.
To enhance the accuracy of wind power forecasting, this paper proposes a hybrid model that integrates Variational Mode Decomposition (VMD), Improved Dung Beetle Optimization (IDBO) and Support Vector Machine (SVM). First, to reduce the volatility and non-stationarity of wind power data, VMD is applied to decompose the original signal into several intrinsic mode functions (IMFs). Subsequently, the Dung Beetle Optimization (DBO) algorithm is improved using chaotic mapping, a Lévy flight search strategy and adaptive t-distribution. Finally, the penalty coefficient of the SVM is optimized using IDBO, and the VMD-IDBO-SVM model is constructed. This study proposes an improved IDBO algorithm and, for the first time, integrates VMD and IDBO-SVM within the context of wind power forecasting. Experimental results show that the proposed VMD-IDBO-SVM model achieves a MAE of 3.315, an RMSE of 4.130, and an R2 of 0.985 on test data from a wind farm, demonstrating a significant improvement compared with the traditional SVM model. It has demonstrated excellent stability and significance in both multi-time-slice validation and statistical testing. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Electric Power Systems)
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17 pages, 1531 KB  
Article
Fine-Grained Segmentation Method of Ground-Based Cloud Images Based on Improved Transformer
by Lihua Zhang, Dawei Shi, Pengfei Li, Buwei Liu, Tongmeng Sun, Bo Jiao, Chunze Wang, Rongda Zhang and Chaojun Shi
Electronics 2026, 15(1), 156; https://doi.org/10.3390/electronics15010156 - 29 Dec 2025
Viewed by 182
Abstract
Solar irradiance is one of the main factors affecting the output of photovoltaic power stations. The cloud distribution above the photovoltaic power station can determine the strength of the absorbed solar irradiance. Cloud estimation is another important factor affecting the output of photovoltaic [...] Read more.
Solar irradiance is one of the main factors affecting the output of photovoltaic power stations. The cloud distribution above the photovoltaic power station can determine the strength of the absorbed solar irradiance. Cloud estimation is another important factor affecting the output of photovoltaic power stations. Ground-based cloud automation observation is an important means to achieve cloud estimation and cloud distribution. Ground-based cloud image segmentation is an important component of ground-based cloud image automation observation. Most of the previous ground-based cloud image segmentation methods rely on convolutional neural networks (CNNs) and lack modeling of long-distance dependencies. In view of the rich fine-grained attributes in ground-based cloud images, this paper proposes a new Transformer architecture for ground-based cloud image fine-grained segmentation based on deep learning technology. The model consists of an encoder–decoder. In order to further mine the fine-grained features of the image, the BiFormer Block is used to replace the original Transformer; in order to reduce the model parameters, the MLP is used to replace the original bottleneck layer; and for the local features of the ground-based cloud, a multi-scale dual-attention (MSDA) block is used to integrate in the jump connection, so that the model can further extract local features and global features. The model is analyzed from both quantitative and qualitative aspects. Our model achieves the best segmentation accuracy, with mIoU reaching 65.18%. The ablation experiment results prove the contribution of key components to segmentation accuracy. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Electric Power Systems)
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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 348
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
Cited by 1 | Viewed by 387
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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