Long Shen
Papers
1
Total Citations
3
H-Index
1
About
Long Shen is a researcher at the forefront of applying deep learning to power system automation and intelligent monitoring. His work focuses on enhancing the reliability and efficiency of electrical substations through advanced computer vision techniques. Shen's most notable contribution is the development of a transfer learning-based YOLOv4 network for the state identification of isolation switches, a critical component in substations. This work, published in 2022 and garnering 3 citations, addresses the growing demand for 24/7 substation monitoring by enabling automated, real-time detection of switch positions—a task traditionally reliant on manual inspection. By leveraging transfer learning, Shen’s approach reduces the need for extensive labeled datasets, making it both practical and scalable for real-world deployment. His research sits at the intersection of power engineering and artificial intelligence, offering a pathway to smarter, more resilient energy infrastructure. Shen’s work is particularly relevant as power systems undergo digital transformation, and his methods hold promise for broader applications in industrial automation.
Research Focus
Key Achievements
Top Papers
- 1