Libin Tan
Papers
1
Total Citations
2
H-Index
1
About
Libin Tan is a researcher focused on advancing intelligent monitoring and fault diagnosis in electrical power systems, with a particular emphasis on deep learning applications for substation equipment. His most-cited work, "Analysis of Frozen Data Anomaly and Update Method of Electromechanical Energy Meter Terminal based on Deep Learning" (2024), addresses critical gaps in substation fault detection by proposing an autonomous monitoring and diagnosis system tailored to real-world operational environments. This contribution tackles the pressing need for more sophisticated, automated detection technologies in aging power infrastructure, where traditional methods fall short. Tan’s research integrates deep learning to enhance the accuracy and timeliness of identifying anomalies like frozen data in energy meter terminals, directly improving grid reliability and maintenance efficiency. With 2 citations to date, his work is gaining traction among peers seeking practical, AI-driven solutions for substation automation. By bridging the gap between advanced computational models and field-deployed systems, Tan is helping to modernize power grid management, making his research particularly valuable for engineers and researchers working on smart grid technologies and predictive maintenance.
Research Focus
Key Achievements
Top Papers
- 1