4 papers
Expanding the Transient Stability Region of Attraction of Networked Grid-Interactive Inverters: A Probabilistic Active Learning Framework
Zhong Liu, Jialin Zheng, Junjie Qin +1
The continuous integration of inverter-based resources makes transient stability analysis increasingly important for power system modernization, in light of the intricate dynamics…
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…
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…
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…