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: 20 January 2026 | Viewed by 32
Special Issue Editors
Interests: computer vision; operation situational awareness; abnormality monitoring of distributed generation equipment
Interests: electric power artificial intelligence; renewable energy utilization; nonlinear degradation behavior prediction
Special Issues, Collections and Topics in MDPI journals
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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Keywords
- electric power artificial intelligence
- computer vision
- multimodal data processing
- smart grid optimization
- large language models
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