Qifan Chen

Qifan Chen's research focuses on power system stability, monitoring, and control, using data-driven and machine learning methods such as neural networks to assess voltage and frequency stability, detect cyberattacks on grid equipment, and forecast power flows and renewable energy output. The work also extends to broader applications of AI and sensing methods, including fault diagnosis, image-based defect detection, and remote sensing. This profile suggests a student would work at the intersection of power engineering and machine learning, applying computational tools to electrical grid problems.

Publication output has grown notably since 2022, rising from about 1 paper per year to a steady 5-6 per year through 2023-2026, suggesting an increasing and currently active research pace.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026