Zhenxing Lin
Papers
2
Total Citations
12
H-Index
2
About
Zhenxing Lin is a researcher at the forefront of intelligent power systems, specializing in the integration of robotics and deep learning for distribution network automation. His work addresses critical challenges in grid reliability by developing advanced fault diagnosis and self-healing methodologies. Lin’s most cited paper, "Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning" (2022, 8 citations), introduces a novel hybrid approach that leverages robotic platforms and deep neural networks to automate equipment image recognition, significantly reducing manual inspection dependency. Building on this, his 2023 study "Method of Fault Self-Healing in Distribution Network and Deep Learning Under Cloud Edge Architecture" (4 citations) proposes a cloud-edge collaborative framework that enhances feature extraction and accuracy in fault recovery, overcoming limitations of conventional deep learning methods. Though early in his career, Lin’s contributions are notable for their practical impact on smart grid resilience, merging physical robotics with AI to enable real-time, autonomous fault management. His work is particularly relevant for researchers exploring edge computing, robotic inspection, and deep learning applications in energy infrastructure, offering a scalable pathway toward more efficient and self-healing electrical distribution systems.
Research Focus
Key Achievements
Top Papers
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- 2