3 papers
eess.SY2026
Lipschitz-Enforced Machine Learning Framework for Accelerating Transient Stability Analysis of Networked Grid-Interactive Inverters
Zhong Liu, Jialin Zheng, Xiaonan Lu
The growing penetration of grid-connected inverters renders Transient Stability Analysis (TSA) increasingly challenging in modern power systems. Existing TSA methodologies encounte…
eess.SY2026
Discovering Unknown Inverter Governing Equations via Physics-Informed Sparse Machine Learning
Jialin Zheng, Ruhaan Batta, Zhong Liu +1
Discovering the unknown governing equations of grid-connected inverters from external measurements holds significant attraction for analyzing modern inverter-intensive power system…
eess.SY2025
Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Jialin Zheng, Zhong Liu, Xiaonan Lu
Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To so…