activity
20192022
most citedData-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

9 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2022

Tie-line Security Regions in High Dimension for Renewable Accommodations

Wei Lin, Hua Jiang, Zhifang Yang

Tie-line power exchanges among regional power systems facilitate renewable accommodations. Power exchanges can be calculated via a tie-line security region that provides the feasib…

eess.SY2020

Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

Xingyu Lei, Zhifang Yang, Junbo Zhao +1

Chance-constrained optimization (CCO) has been widely used for uncertainty management in power system operation. With the prevalence of wind energy, it becomes possible to consider…

eess.SY2020★ 9 cited

Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

Xingyu Lei, Zhifang Yang, Juan Yu +3

This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not…

eess.SP2019★ 1 cited

Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach

Yan Yang, Juan Yu, Zhifang Yang +2

Probabilistic optimal power flow (POPF) is an important analytical tool to ensure the secure and economic operation of power systems. POPF needs to solve enormous nonlinear and non…

eess.SP2019★ 9 cited

Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach

Yan Yang, Zhifang Yang, Juan Yu +1

Probabilistic power flow (PPF) plays a critical role in power system analysis. However, the high computational burden makes it challenging for the practical implementation of PPF.…