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Applied Sciences
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Applied Computer Vision and Intelligent Computing for Electric Power Systems
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Applied Computer Vision and Intelligent Computing for Electric Power Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 10 August 2026 | Viewed by 1450

Special Issue Editors

Dr. Yunfeng Yan

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Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: computer vision; operation situational awareness; abnormality monitoring of distributed generation equipment
Dr. Xian-Bo Wang

E-Mail Website
Guest Editor
Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
Interests: electric power artificial intelligence; renewable energy utilization; nonlinear degradation; behavior prediction; fault diagnosis; object detection
Special Issues, Collections and Topics in MDPI journals
Dr. Yulin Chen

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Guest Editor
Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
Interests: self optimal control of active distribution network; cyber-physical security with application in power system; survivability of power system under extreme conditions

Special Issue Information

Dear Colleagues,

With the acceleration of the global energy transition, power systems are undergoing profound transformations, marked by large-scale renewable energy integration, new energy access, and the tight coupling of energy–carbon markets. These changes introduce unprecedented complexity, characterized by dynamic uncertainties, abrupt fluctuations, and intricate inter-dependencies, necessitating breakthroughs in operational intelligence and resilient scheduling. Computer vision and artificial intelligence, with their powerful data processing capabilities and highly adaptive learning characteristics, have brought revolutionary improvements to the perception accuracy and decision-making efficiency of power systems. Furthermore, AI's data-driven insights and adaptive learning capabilities enhance the accuracy of power system situational awareness and operational decision-making, while computer vision enables real-time equipment inspection and anomaly detection through image-based analysis. However, in this process, challenges such as cross-domain collaboration of multi-source power data and the application of explainable AI algorithms need to be addressed. It is necessary to study multi-modal power data fusion mechanisms, human–machine collaboration and autonomous decision-making mechanisms in power systems, as well as automated monitoring and fault diagnosis technologies for electrical equipment. Through these technologies, we aim to provide technical references for the operation of power systems towards safer, greener, and more autonomous directions.

Dr. Yunfeng Yan
Dr. Xian-Bo Wang
Dr. Yulin Chen
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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
  • computer vision
  • multimodal data processing
  • smart grid optimization
  • large language models

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Published Papers (2 papers)

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Research

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15 pages, 1093 KB  
Article
A Multimodal Power Sample Feature Migration Method Based on Dual Cross-Modal Information Decoupling
by Zhenyu Chen, Huaguang Yan, Jianguang Du, Yuhao Zhou, Yi Chen, Yunfeng Yan and Shuai Zhao
Appl. Sci. 2025, 15(18), 9913; https://doi.org/10.3390/app15189913 - 10 Sep 2025
Viewed by 1005
Abstract
With the rapid development of energy transition and power system informatization, the efficient integration and feature migration of multimodal power data have become critical challenges for intelligent power systems. Existing methods often overlook fine-grained semantic relationships in cross-modal alignment, leading to low information [...] Read more.
With the rapid development of energy transition and power system informatization, the efficient integration and feature migration of multimodal power data have become critical challenges for intelligent power systems. Existing methods often overlook fine-grained semantic relationships in cross-modal alignment, leading to low information utilization. This paper proposes a multimodal power sample feature migration method based on dual cross-modal information decoupling. By introducing a fine-grained image–text alignment strategy and a dual-stream attention mechanism, deep integration and efficient migration of multimodal features are achieved. Experiments demonstrate that the proposed method outperforms baseline models (e.g., LLaVA, Qwen) in power scenario description (CSD), event localization (CELC), and knowledge question answering (CKQ), with significant improvements of up to 12.8% in key metrics such as image captioning (IC) and grounded captioning (GC). The method provides a robust solution for multimodal feature migration in power inspection and real-time monitoring, showing high practical value in industrial applications. Full article
(This article belongs to the Special Issue Applied Computer Vision and Intelligent Computing for Electric Power Systems)
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Review

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38 pages, 2857 KB  
Review
BIM-Based Digital Twin and Extended Reality for Electrical Maintenance in Smart Buildings: A Structured Review with Implementation Evidence
by Paolo Di Leo, Michele Zucco and Matteo Del Giudice
Appl. Sci. 2026, 16(8), 3685; https://doi.org/10.3390/app16083685 - 9 Apr 2026
Abstract
The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly [...] Read more.
The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article
(This article belongs to the Special Issue Applied Computer Vision and Intelligent Computing for Electric Power Systems)
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