Papers
1
Total Citations
7
H-Index
1
About
Xin Shan is a researcher at the forefront of applying artificial intelligence to power grid operations, with a particular focus on deep learning-based automation and fault response. His most-cited work, “Research on Deep Learning Based Dispatching Fault Disposal Robot Technology” (2018, 7 citations), addresses a critical challenge in modern energy systems: the need for real-time, intelligent decision-making in large-scale power grid dispatching. By integrating deep learning with robotic process automation, Shan’s research explores how AI can handle the complex, knowledge-intensive tasks of fault detection and disposal—traditionally reliant on human expertise and online analysis. This work has implications for enhancing grid resilience and reducing response times during outages. While his citation count reflects a niche but growing field, Shan’s contributions are notable for bridging cutting-edge AI techniques with practical, high-stakes infrastructure challenges. His research speaks to the future of autonomous energy management, where intelligent systems support human operators in maintaining the stability of increasingly complex power networks.
Research Focus
Key Achievements
Top Papers
- 1